Publications
International refereed journals
Modern literature exhibits numerous centralized control approaches—event-based or model assisted—for tackling poor energy performance in buildings. Unfortunately, even novel building optimization and control (BOC) strategies commonly suffer from complexity and scalability issues as well as uncertain behavior as concerns large-scale building ecosystems—a fact that hinders their practical compatibility and broader applicability. Moreover, decentralized optimization and control approaches trying to resolve scalability and complexity issues have also been proposed in literature. Those approaches usually suffer from modeling issues, utilizing an analytically available formula for the overall performance index. Motivated by the complications in existing strategies for BOC applications, a novel, decentralized, optimization and control approach—referred to as Local for Global Parameterized Cognitive Adaptive Optimization (L4GPCAO)—has been extensively evaluated in a simulative environment, contrary to previous constrained real-life studies. The current study utilizes an elaborate simulative environment for evaluating the efficiency of L4GPCAO; extensive simulation tests exposed the efficiency of L4GPCAO compared to the already evaluated centralized optimization strategy (PCAO) and the commercial control strategy that is adopted in the BOC practice (common reference case). L4GPCAO achieved a quite similar performance in comparison to PCAO (with 25% less control parameters at a local scale), while both PCAO and L4GPCAO significantly outperformed the reference BOC practice.
The upcoming high population density rise in metropolitan areas is anticipated to further deteriorate the traffic conditions. To tackle this problem, advanced ICT applications have been employed, able to monitor and manage traffic in real time. In practice, to efficiently correspond to dynamic traffic conditions those applications require to be frequently reconfigured – an operation that usually involves expert-teams manually adjusting the traffic-regulating strategies regularly. However, these manual procedures are not adequately aligned with the traffic situation since complicated stochastic dynamics, model unavailability and data inner-transmission constraints usually emerge. In order to overcome such cumbersome and expensive adjustment procedures modern decentralized adaptive optimization is widely accepted and recognized as an efficient automated solution for tuning the control strategy on-the-fly. Motivated by the above, L4GCAO, a decentralized, model independent, flexible optimization technique has been designed for optimizing cycle management at a local level to improve network performance at the global level, by automatically adjusting the cycle-regulating parameters in an intersection-centric manner, through cooperating self-learning agents.
This paper studies L4GCAO’s first application on a realistic traffic-network simulation scheme that examines the online fine-tuning process of the cycle-regulating parameters. Moreover, in order to evaluate the decentralized L4GCAO performance, two levels of performance benchmarking have been considered: a comparison with CAO – its well-established centralized counterpart; an already well-designed fixed-time management plan. In all cases, L4GCAO exhibits an almost equivalent performance improvement compared to CAO, both with respect to a properly fixed-time traffic management plan, while utilizes less parameters in a non-centralized manner.
This paper tackles the problem of positioning a swarm of UAVs inside a completely unknown terrain, having as objective to maximize the overall situational awareness. The situational awareness is expressed by the number and quality of unique objects of interest, inside the UAVs’ fields of view. YOLOv3 and a system to identify duplicate objects of interest were employed to assign a single score to each UAVs’ configuration. Then, a novel navigation algorithm, capable of optimizing the previously defined score, without taking into consideration the dynamics of either UAVs or environment, is proposed. A cornerstone of the proposed approach is that it shares the same convergence characteristics as the block coordinate descent (BCD) family of approaches. The effectiveness and performance of the proposed navigation scheme were evaluated utilizing a series of experiments inside the AirSim simulator. The experimental evaluation indicates that the proposed navigation algorithm was able to consistently navigate the swarm of UAVs to “strategic” monitoring positions and also adapt to the different number of swarm sizes.
This paper presents a distributed algorithm applicable to a wide range of practical multi-robot applications. In such multi-robot applications, the user-defined objectives of the mission can be cast as a general optimization problem, without explicit guidelines of the subtasks per different robot. Owing to the unknown environment, unknown robot dynamics, sensor nonlinearities, etc., the analytic form of the optimization cost function is not available a priori. Therefore, standard gradient-descent-like algorithms are not applicable to these problems. To tackle this, we introduce a new algorithm that carefully designs each robot’s subcost function, the optimization of which can accomplish the overall team objective. Upon this transformation, we propose a distributed methodology based on the cognitive-based adaptive optimization (CAO) algorithm, that is able to approximate the evolution of each robot’s cost function and to adequately optimize its decision variables (robot actions). The latter can be achieved by online learning only the problem-specific characteristics that affect the accomplishment of mission objectives. The overall, low-complexity algorithm can straightforwardly incorporate any kind of operational constraint, is fault tolerant, and can appropriately tackle time-varying cost functions. A cornerstone of this approach is that it shares the same convergence characteristics as those of block coordinate descent algorithms. The proposed algorithm is evaluated in three heterogeneous simulation set-ups under multiple scenarios, against both general-purpose and problem-specific algorithms.
Building Automation (BA) is key to encourage the growth of more sustainable cities and smart homes. However, current BA systems are not able to manage new constructions based on Adaptable/Dynamic Building Envelopes (ADBE) achieving near-zero energy-efficiency. The ADBE buildings integrate Renewable Energy Sources (RES) and Envelope Retrofitting (ER) that must be managed by new BA systems based on Artificial Intelligence (AI) and Internet of Things (IoT) through secure protocols. This paper presents the PLUG-N-HARVEST architecture based on cloud AI systems and security-by-design IoT networks to manage near-zero ADBE constructions in both residential and commercial buildings. To demonstrate the PLUG-N-HARVEST architecture, three different real-world pilots have been considered in Germany, Greece and Spain. The paper describes the Spain pilot of residential buildings including the deployment of IoT wireless networks (i.e., sensors and actuators) based on Zwave technology to enable plug-and-play installations. The real-world tests showed the high efficiency of security-by-design Internet communications between building equipment and cloud management systems. Moreover, the results of cloud intelligent management demonstrate the improvements in both energy consumption and comfort conditions.
A variety of novel, recyclable and reusable, construction materials has already been studied within literature during the past years, aiming at improving the overall energy efficiency ranking of the building envelope. However, several studies show that a delicate control of indoor climating elements can lead to a significant performance improvement by exploiting the building’s savings potential via smart adaptive HVAC regulation to exogenous uncertain disturbances (e.g. weather, occupancy). Building Optimization and Control (BOC) systems can be categorized into two different groups: centralized (requiring high data transmission rates at a central node from every corner of the overall system) and decentralized1 (assuming an intercommunication among neighboring constituent systems). Moreover, both approaches can be further divided into two subcategories, respectively: model-assisted (usually introducing modeling oversimplifications) and model-free (typically presenting poor stability and very slow convergence rates). This paper presents the application of a novel, decentralized, agent-based, model-free BOC methodology (abbreviated as L4GPCAO) to a modern non-residential building (E.ON. Energy Research Center’s main building), equipped with controllable HVAC systems and renewable energy sources by utilizing the existing Building Management System (BES). The building testbed is located inside the RWTH Aachen University campus in Aachen, Germany. A combined rule criterion composed of the non-renewable energy consumption (NREC) and the thermal comfort index – aligned to international comfort standards – was adopted in all cases presented herein. Besides the limited availability of the specified building testbed, real-life experiments demonstrated operational effectiveness of the proposed approach in BOC applications with complex, emerging dynamics arising from the building’s occupancy and thermal characteristics. L4GPCAO outperformed the control strategy that was designed by the planers and system provider, in a conventional manner, requiring no more than five test days.
Managing grid-connected charging stations for fleets of electric vehicles leads to an optimal control problem where user preferences must be met with minimum energy costs (e.g., by exploiting lower electricity prices through the day, renewable energy production, and stored energy of parked vehicles). Instead of state-of-the-art charging scheduling based on open-loop strategies that explicitly depend on initial operating conditions, this paper proposes an approximate dynamic programming feedback-based optimization method with continuous state space and action space, where the feedback action guarantees uniformity with respect to initial operating conditions, while price variations in the electricity and available solar energy are handled automatically in the optimization. The resulting control action is a multi-modal feedback, which is shown to handle a wide range of operating regimes, via a set of controllers whose action that can be activated or deactivated depending on availability of solar energy and pricing model. Extensive simulations via a charging test case demonstrate the effectiveness of the approach.
Load management actions in large buildings are pre-programmed by field engineers/users in the form of if-then-else rules for the set point of the thermostat. This fixed set of actions prevents smart zoning, i.e. to dynamically regulate the set points in every room at different levels according to geometry, orientation and interaction among rooms caused by occupancy patterns. In this work we frame the problem of load management with smart zoning into a multiple-mode feedback-based optimal control problem: multiple-mode refers to embedding multiple behaviors (triggered by building-occupant dynamic interaction) into the optimization problem; feedback-based refers to adopting a Hamilton-Jacobi-Bellman framework, with closed-loop control strategies using information stemming from building and weather states. The framework is solved by parameterizing the candidate control strategies and by searching for the optimal strategy in an adaptive self-tuning way. To demonstrate the proposed approach, we employ an EnergyPlus model of an actual office building in Crete, Greece. Extensive tests show that the proposed solution is able to provide, dynamically and autonomously, dedicated set points levels in every room in such a way to optimize the whole building performance (exploitation of renewable energy sources with improved thermal comfort). As compared to pre-programmed (non-optimal) strategies, we show that smart zoning makes it is possible to save more than 15% energy consumption, with 25% increased thermal comfort. As compared to optimized strategies in which smart zoning is not implemented, smart zoning leads to additional 4% reduced energy and 8% improved comfort, demonstrating improved occupant-building interaction. Such improvements are motivated by the fact that the approach exploits the building dynamics as learned from feedback data. Moreover, the closed-loop feature of the approach makes it robust to variable weather conditions and occupancy schedules.
This paper deals with the path planning problem of a team of mobile robots, in order to cover an area of interest, with prior-defined obstacles. For the single robot case, also known as single robot coverage path planning (CPP), an 𝓞(n) optimal methodology has already been proposed and evaluated in the literature, where n is the grid size. The majority of existing algorithms for the multi robot case (mCPP), utilize the aforementioned algorithm. Due to the complexity, however, of the mCPP, the best the existing mCPP algorithms can perform is at most 16 times the optimal solution, in terms of time needed for the robot team to accomplish the coverage task, while the time required for calculating the solution is polynomial. In the present paper, we propose a new algorithm which converges to the optimal solution, at least in cases where one exists. The proposed technique transforms the original integer programming problem (mCPP) into several single-robot problems (CPP), the solutions of which constitute the optimal mCPP solution, alleviating the original mCPP explosive combinatorial complexity. Although it is not possible to analytically derive bounds regarding the complexity of the proposed algorithm, extensive numerical analysis indicates that the complexity is bounded by polynomial curves for practical sized inputs. In the heart of the proposed approach lies the DARP algorithm, which divides the terrain into a number of equal areas each corresponding to a specific robot, so as to guarantee complete coverage, non-backtracking solution, minimum coverage path, while at the same time does not need any preparatory stage
This paper introduces a low-cost, high-quality Decision Making Mechanism for supporting the tasks of temperature regulation of existing HVAC installations in a smart building environment. It incorporates Artificial Neural Networks and Fuzzy Logic in order to improve the occupants’ thermal comfort while maintaining the total energy consumption. Contrary to existing approaches, it focuses in achieving significantly low computational complexity, which in turn enables its hardware implementation onto low-cost embedded platforms, such the ones used in smart thermostats. Both the software components and hardware implantation are described in detail. To demonstrate its effectiveness, the proposed method was compared to ruled-based controllers, as well as state-of-the-art control techniques. A simulation model was developed using the EnergyPlus building simulation suite, a detailed modeled micro-grid environment of buildings located in Chania Greece and historic weather and energy pricing data. Simulation results validate the effectiveness of our approach.
This paper deals with the problem of autonomous exploration of unknown areas using teams of Autonomous X Vehicles (AXVs)—with X standing for Aerial, Underwater or Sea-surface—where the AXVs have to autonomously navigate themselves so as to construct an accurate map of the unknown area. Such a problem can be transformed into a dynamic optimization problem which, however, is NP complete and thus infeasible to be solved. A usual attempt is to relax this problem by employing greedy (optimal one-step-ahead) solutions which may end up quite problematic. In this paper, we first show that optimal one-step ahead exploration schemes that are based on a transformed optimization criterion can lead to highly efficient solutions to the multi-AXV exploration. Such a transformed optimization criterion is constructed using both theoretical analysis and experimental investigations and attempts to minimize the “disturbing” effect of deadlocks and nonlinearities to the overall exploration scheme. As, however, optimal one-step-ahead solutions to the transformed optimization criterion cannot be practically obtained using conventional optimization schemes, the second step in our approach is to combine the use of the transformed optimization criterion with the cognitive adaptive optimization (CAO): CAO is a practicably feasible computational methodology which adaptively provides an accurate approximation of the optimal one-step-ahead solutions. The combination of the transformed optimization criterion with CAO results in a multi-AXV exploration scheme which is both practically implementable and provides with quite efficient solutions as it is shown both by theoretical analysis and, most importantly, by extensive simulation experiments and real-life underwater sea-floor mapping experiments in the Leixes port, Portugal.
Building energy consumption used for internal heating and cooling purposes is one of the most viral research topics. Retrofitting and renovation activities in building applications aim towards utilizing modern construction materials, with improved thermal and insulation characteristics. It is more than evident that such an approach leads to an improved thermal shield for the building (improving passive building elements). In addition well calibrated rule based control designs are also being adopted in the last decades as a way to improve the energy efficiency in buildings (improving active elements management). Both of the above approaches though are considered as time static since disturbances with high uncertainty (weather conditions, human presence and activity) along with the unavoidable construction material aging phenomena affect building behavior and HVAC dynamics. As a result control recalibration activities seem more than necessary to maintain energy efficiency. Followed by the rapid evolution in the computing machines sector and simulation software kits, research effort has been focused on model-assisted and co-simulation based control strategies which utilize the available computational power of modern machines towards improving energy efficiency and comfort levels through appropriate design Building Optimization and Control (BOC) systems, utilizing available system models. However the main drawback in model-assisted strategies is the fact that they heavily rely on the available building model which requires a tedious offline pre-application period including many simulation tests and/or field experiments so as to fine tune and tailor manually the model and consequently the control logic implemented. Moreover, no matter how elaborate the building model is, aging characteristics and uncertain disturbances are factors which call for re-designing (periodically) the available simulation model and the respective control strategies.
This paper considers an alternative approach to BOC system design. The main attribute of the proposed methodology is that it can provide automated fine-tuning of the BOC system: no human intervention or a simulation model are required for the initial deployment of the controller as well as for the continuously applied fine-tuning procedure. Real-life experiments performed in a highly energy demanding building in Tel Aviv Israel, during spring time, demonstrate that the proposed approach can effectively provide intelligent decisions that none of the currently employed rule/event-based strategy can replicate.
Integration of renewable energy sources in microgrids can be achieved via demand response programs, which change the electric usage in response to changes in the availability and price of electricity over time. This paper presents a novel control algorithm for joint demand response management and thermal comfort optimization in microgrids equipped with renewable energy sources and energy storage units. The proposed work aims at covering two main gaps in current state-of-the-art demand response programs. The first gap is integrating the objective of matching energy generation and consumption with the occupant behavior and with the objective of guaranteeing thermal comfort of the occupants. The second gap is developing a scalable and robust demand response program. Large-scale nature of the optimization problem and robustness are achieved via a two-level supervisory closed-loop feedback strategy: at the lower level, each building of the microgrid employs a local closed-loop feedback controller that processes only local measurements; at the upper level, a centralized unit supervises and updates the local controllers with the aim of minimizing the aggregate energy cost and thermal discomfort of the microgrid. The effectiveness of the proposed method is validated in a microgrid composed of three buildings, a photovoltaic array, a wind turbine, and an energy storage unit. Comparisons with alternative demand response strategies reveal that the proposed strategy efficiently integrates the renewable sources; energy costs are reduced and at the same time thermal comfort of the occupants is guaranteed. Furthermore, robustness is proved via consistent improvements achieved under heterogeneous conditions (different occupancy schedules and different weather conditions).
This paper deals with the problem of autonomous exploration of unknown areas using teams of Autonomous X Vehicles (AXVs)—with X standing for Aerial, Underwater or Sea-surface—where the AXVs have to autonomously navigate themselves so as to construct an accurate map of the unknown area. Such a problem can be transformed into a dynamic optimization problem which, however, is NP-complete and thus infeasible to be solved. A usual attempt is to relax this problem by employing greedy (optimal one-step-ahead) solutions which may end-up quite problematic. In this paper, we first show that optimal one-step-ahead exploration schemes that are based on a transformed optimization criterion can lead to highly efficient solutions to the multi-AXV exploration. Such a transformed optimization criterion is constructed using both theoretical analysis and experimental investigations and attempts to minimize the “disturbing” effect of deadlocks and nonlinearities to the overall exploration scheme. As, however, optimal one-step-ahead solutions to the transformed optimization criterion cannot be practically obtained using conventional optimization schemes, the second step in our approach is to combine the use of the transformed optimization criterion with the cognitive adaptive optimization (CAO): CAO is a practicably feasible computational methodology which adaptively provides an accurate approximation of the optimal one-step-ahead solutions. The combination of the transformed optimization criterion with CAO results in a multi-AXV exploration scheme which is both practically implementable and provides with quite efficient solutions as it is shown both by theoretical analysis and, most importantly, by extensive simulation experiments and real-life underwater sea-floor mapping experiments in the Leixes port, Portugal.
Energy efficient passive designs and constructions have been extensively studied in the last decades as a way to improve the ability of a building to store thermal energy, increase its thermal mass, increase passive insulation and reduce heat losses. However, many studies show that passive thermal designs alone are not enough to fully exploit the potential for energy efficiency in buildings: in fact, harmonizing the active elements for indoor thermal comfort with the passive design of the building can lead to further improvements in both energy efficiency and comfort. These improvements can be achieved via the design of appropriate Building Optimization and Control (BOC) systems, a task which is more complex in high-inertia buildings than in conventional ones. This is because high thermal mass implies a high memory, so that wrong control decisions will have negative repercussions over long time horizons. The design of proactive control strategies with the capability of acting in advance of a future situation, rather than just reacting to current conditions, is of crucial importance for a full exploitation of the capabilities of a high-inertia building. This paper applies a simulation-assisted control methodology to a high-inertia building in Kassel, Germany. A simulation model of the building is used to proactively optimize, using both current and future information about the external weather condition and the building state, a combined criterion composed of the energy consumption and the thermal comfort index. Both extensive simulation as well as real-life experiments performed during the unstable German wintertime, demonstrate that the proposed approach can effectively deal with the complex dynamics arising from the high-inertia structure, providing proactive and intelligent decisions that no currently employed rule-based strategy can replicate.
Electrical smart microgrids equipped with small-scale renewable-energy generation systems are emerging progressively as an alternative or an enhancement to the central electrical grid: due to the intermittent nature of the renewable energy sources, appropriate algorithms are required to integrate these two typologies of grids and, in particular, to perform efficiently dynamic energy demand and distributed generation management, while guaranteeing satisfactory thermal comfort for the occupants. This paper presents a novel control algorithm for joint energy demand and thermal comfort optimization in photovoltaic-equipped interconnected microgrids. Energy demand shaping is achieved via an intelligent control mechanism for heating, ventilating, and air conditioning units. The intelligent control mechanism takes into account the available solar energy, the building dynamics and the thermal comfort of the buildings’ occupants. The control design is accomplished in a simulation-based fashion using an energy simulation model, developed in EnergyPlus, of an interconnected microgrid. Rather than focusing only on how each building behaves individually, the optimization algorithm employs a central controller that allows interaction among the buildings of the microgrid. The control objective is to optimize the aggregate microgrid performance. Simulation results demonstrate that the optimization algorithm efficiently integrates the microgrid with the photovoltaic system that provides free electric energy: in particular, for each building composing the microgrid, the energy absorbed from the main grid is minimized, the energy demand is balanced with the solar energy delivered to each building, while taking into account the thermal comfort of the occupants.
Energy efficient operation of microgrids, a localized grouping of controllable loads with distributed energy resources like solar photovoltaic panels, requires the development of energy management systems (EMSs) with the capability of controlling the loads so as to optimize the aggregate performance of the microgrid. In microgrids comprising of buildings of different nature (residential, commercial, industrial, etc.), where the occupants exhibit heterogeneous occupancy schedules, the objective of an effective management strategy is to optimize the aggregate performance by intelligently exploiting the occupancy schedules and the intermittent production of solar energy. This paper presents a simulation-based optimization approach for the design of an EMS in grid-connected photovoltaic-equipped microgrids with heterogeneous occupancy schedule. The microgrid exchanges energy, buying or selling it, with the main grid and the EMS optimizes an aggregate multi-objective criterion that takes into account both the energy cost and the thermal comfort of the occupants of the microgrid. Simulative results obtained using a microgrid test case developed in EnergyPlus demonstrate the effectiveness of the proposed approach: the proposed EMS strategy is shown to take advantage of the occupancy information, intelligently and automatically changing the energy demand of each building according to the occupants’ behavior, and achieving relevant improvements with respect to alternative EMS strategies.
This article describes a computationally efficient simulation-based control design approach that has the capability of handling optimization problems arising from large-scale nonlinear systems, with fast convergence properties and low computational requirements. The purpose of this article is to describe the main features of the PCAO algorithm, analyze its convergence and stability properties, and demonstrate its efficiency using simulations of two large-scale, real-life systems (a traffic network and an energy-efficient building) for which conventional optimization techniques fail to provide an efficient simulation-based control design.
Accurate maps are essential in the case of robot teams, so that they can operate autonomously and accomplish their tasks efficiently. In this work we present an approach which allows the generation of detailed maps, suitable for robot navigation, from a mesh of sparse points using Cellular Automata and simple evolutions rules. The entire map area can be considered as a 2D Cellular Automaton (CA) where the value at each CA cell represents the height of the ground in the corresponding coordinates. The set of measurements form the original state of the CA. The CA rules are responsible for generating the intermediate heights among the real measurements. The proposed method can automatically adjust its rules, so as to encapture local morphological attributes, using a pre-processing procedure in the set of measurements. The main advantage of the proposed approach is the ability to maintain an accurately reconstruction even in cases where the number of measurements are significant reduced. Experiments have been conducted employing data collected from two totally different real-word environments. In the first case the proposed approach is applied, so as to build a detailed map of a large unknown underwater area in Oporto, Portugal. The second case concerns data collected by a team of aerial robots in real experiments in an area near Zurich, Switzerland and is also used for the evaluation of the approach. The data collected, in the two aforementioned cases, are extracted using different kind of sensors and robots, thus demonstrating the applicability of our approach in different kind of devices. The proposed method outperforms the performance of other well-known methods in literature thus enabling its application for real robot navigation.
The smart grid technology is emerging progressively, due to environmental and energy standards and to the forecasted exhaustion of non-renewable energy sources: currently, many smart microgrids small-scale renewable-energy generation systems are connected to the traditional grid, allowing the two grids to coexist. To perform efficiently load control and demand response management, appropriate algorithm for controlling smart microgrids must be developed. In addition to load control for reduction of energy consumption, climate control to optimize the thermal comfort of the occupants must be performed. This paper presents a microgrid simulation test case to evaluate the performance of different strategies for load management and thermal comfort optimization. The main contribution of the paper are: (1) contrary to many state-of the-art approaches that rely on simplified model, in this papers a realistic microgrid, modelled using an elaborate energy building simulation program (EnergyPlus) is employed for the synthesis and evaluation of the control strategies; (2) a realistic thermal comfort model, the Fanger index, is adopted and the thermal satisfaction of the end user is part of the performance index to be optimized; (3) the optimization of the energy load and of the solar energy distribution is performed jointly; far from optimal solutions can be achieved if one of the two tasks is neglected. Simulations using historical summer data demonstrate that via optimization and control we can effectively integrate the microgrid test case with the photovoltaic system that provides renewable solar energy, i.e. we can optimally balance the energy demand with the solar energy, while taking into account the thermal comfort of the occupants.
This paper describes a new control scheme for approximately optimal control (AOC) of nonlinear systems, convex control design (ConvCD). The key idea of ConvCD is to transform the approximate optimal control problem into a convex semi-definite programming (SDP) problem. Contrary to the majority of existing AOC designs where the problem that is addressed is to provide a control design which approximates the performance of the optimal controller by increasing the “controller complexity,” the proposed approach addresses a different problem: given a controller of “fixed complexity” it provides a control design that renders the controller as close to the optimal as possible and, moreover, the resulted closed-loop system stable. Two numerical examples are used to show the effectiveness of the method.
This paper deals with the problem of deploying a team of flying robots to perform surveillance-coverage missions over a terrain of arbitrary morphology. In such missions, a key factor for the successful completion of the mission is the knowledge of the terrain’s morphology. The focus of this paper is on the implementation of a two-step procedure that allows us to optimally align a team of flying vehicles for the aforementioned task. Initially, a single robot constructs a map of the area using a novel monocular-vision-based approach. A state-of-the-art visual-SLAM algorithm tracks the pose of the camera while, simultaneously, autonomously, building an incremental map of the environment. The map generated is processed and serves as an input to an optimization procedure using the cognitive, adaptive methodology initially introduced in Renzaglia et al. (Proceedings of the IEEE international conference on robotics and intelligent system (IROS), Taipei, Taiwan, pp. 3314–3320, 2010). The output of this procedure is the optimal arrangement of the robots team, which maximizes the monitored area. The efficiency of our approach is demonstrated using real data collected from aerial robots in different outdoor areas.
The problem of deploying a team of flying robots to perform surveillance coverage missions over an unknown terrain of complex and non-convex morphology is presented. In such a mission, the robots attempt to maximize the part of the terrain that is visible while keeping the distance between each point in the terrain and the closest team member as small as possible. A trade-off between these two objectives should be fulfilled given the physical constraints and limitations imposed at the particular application. As the terrain’s morphology is unknown and it can be quite complex and non-convex, standard algorithms are not applicable to the particular problem treated in this paper. To overcome this, a new approach based on the Cognitive-based Adaptive Optimization (CAO) algorithm is proposed and evaluated. A fundamental property of this approach is that it shares the same convergence characteristics as those of constrained gradient-descent algorithms (which require perfect knowledge of the terrain’s morphology and optimize surveillance coverage subject to the constraints the team has to satisfy). Rigorous mathematical arguments and extensive simulations establish that the proposed approach provides a scalable and efficient methodology that incorporates any particular physical constraints and limitations used to navigate the robots into an arrangement that (locally) optimizes surveillance coverage.
Buildings nowadays are increasingly expected to meet higher and more complex performance requirements: they should be sustainable; use zero-net energy; foster a healthy and comfortable environment for the occupants; be grid-friendly, yet economical to build and maintain. The essential ingredients for the successful development and operation of net zero- and positive-energy buildings (NZEB/PEB) are: thermal simulation models, that are accurate representations of the building and its subsystems; sensors, actuators, and user interfaces to facilitate communication between the physical and simulation layers; and finally, integrated control and optimization tools of sufficient generality that using the sensor inputs and the thermal models can take intelligent decisions, in almost real-time, regarding the operation of the building and its subsystems. To this end the aim of the present paper is to present a review on the technological developments in each of the essential ingredients that may support the future integration of successful NZEB/PEB, i.e. accurate simulation models, sensors and actuators and last but not least the building optimization and control. The integration of the user is an integral part in the dynamic behavior of the system, and this role has to be taken into account. Future prospects and research trends are discussed.
In this paper, we study the problem of optimizing (fine-tuning) the design parameters of large-scale traffic control systems that are composed of distinct and mutually interacting modules. This problem usually requires a considerable amount of human effort and time to devote to the successful deployment and operation of traffic control systems due to the lack of an automated well-established systematic approach. We investigate the adaptive fine-tuning algorithm for determining the set of design parameters of two distinct mutually interacting modules of the traffic-responsive urban control (TUC) strategy, i.e., split and cycle, for the large-scale urban road network of the city of Chania, Greece. Simulation results are presented, demonstrating that the network performance in terms of the daily mean speed, which is attained by the proposed adaptive optimization methodology, is significantly better than the original TUC system in the case in which the aforementioned design parameters are manually fine-tuned to virtual perfection by the system operators.
Learning mechanisms that operate in unknown environments should be able to efficiently deal with the problem of controlling unknown dynamical systems. Many approaches that deal with such a problem face the so-called exploitation-exploration dilemma where the controller has to sacrifice efficient performance for the sake of learning “better” control strategies than the ones already known: during the exploration period, poor or even unstable closed-loop system performance may be exhibited. In this paper, we show that, in the case where the control goal is to stabilize an unknown dynamical system by means of state feedback, exploitation and exploration can be concurrently performed without the need of sacrificing efficiency. This is made possible through an appropriate combination of recent results developed by the author in the areas of adaptive control and adaptive optimization and a new result on the convex construction of control Lyapunov functions for nonlinear systems. The resulting scheme guarantees arbitrarily good performance in the regions where the system is controllable. Theoretical analysis as well as simulation results on a particularly challenging control problem verify such a claim.
This technical note tackles the problem of constructing state-feedback stabilizers that guarantee good transient closed-loop performance when applied to general unknown nonlinear multiinput state-feedback stabilizable systems. An adaptive Control Lyapunov Function-based control scheme is proposed in order to address this problem. Mathematical analysis establishes that the proposed control scheme guarantees good closed-loop transient performance outside the regions where the system is uncontrollable, provided the controlled system admits a controllability-like property.
Adaptive optimization (AO) schemes based on stochastic approximation principles such as the Random Directions Kiefer–Wolfowitz (RDKW), the Simultaneous Perturbation Stochastic Approximation (SPSA) and the Adaptive Fine-Tuning (AFT) algorithms possess the serious disadvantage of not guaranteeing satisfactory transient behavior due to their requirement for using random or random-like perturbations of the parameter vector. The use of random or random-like perturbations may lead to particularly large values of the objective function, which may result to severe poor performance or stability problems when these methods are applied to closed-loop controller optimization applications. In this paper, we introduce and analyze a new algorithm for alleviating this problem. Mathematical analysis establishes satisfactory transient performance and convergence of the proposed scheme under a general set of assumptions. Application of the proposed scheme to the adaptive optimization of a large-scale, complex control system demonstrates the efficiency of the proposed scheme.
Multi-objective robot exploration, constitutes one of the most challenging tasks for autonomous robots performing in various operations and different environments. However, the optimal exploration path depends heavily on the objectives and constraints that both hese operations and environments introduce. Typical environment constraints include partially known or completely unknown workspaces, limited-bandwidth communications and sparse or dense clattered spaces. In such environments, the exploration robots must satisfy additional operational constraints including time-critical goals, kinematic modeling and resource limitations. Finding the optimal exploration path under these multiple constraints and objectives constitutes a challenging non-convex optimization problem. In our approach, we model the environment constraints in cost functions and utilize the Cognitive-based Adaptive Optimization (CAO) algorithm in order to meet time-critical objectives. The exploration path produced is optimal in the sense of globally minimizing the required time as well as maximizing the explored area of a partially unknown workspace. Since obstacles are sensed during operation, initial paths are possible to be blocked leading to a robot entrapment. A supervisor is triggered to signal a blocked passage and subsequently escape from the basin of cost function local minimum. Extensive simulations and comparisons in typical scenarios are presented in order to show the efficiency of the proposed approach.
Despite the continuous advances in the fields of intelligent control and computing, the design and deployment of efficient large scale nonlinear control systems (LNCSs) requires a tedious fine-tuning of the LNCS parameters before and during the actual system operation. In the majority of LNCSs the fine-tuning process is performed by experienced personnel based on field observations via experimentation with different combinations of controller parameters, without the use of a systematic approach. The existing adaptive/neural/fuzzy control methodologies cannot be used towards the development of a systematic, automated fine-tuning procedure for general LNCS due to the strict assumptions they impose on the controlled system dynamics; on the other hand, adaptive optimization methodologies fail to guarantee an efficient and safe performance during the fine-tuning process, mainly due to the fact that these methodologies involve the use of random perturbations. In this paper, we introduce and analyze, both by means of mathematical arguments and simulation experiments, a new learning/adaptive algorithm that can provide with convergent, an efficient and safe fine-tuning of general LNCS. The proposed algorithm consists of a combination of two different algorithms proposed by Kosmatopoulos (2007 and 2008) and the incremental-extreme learning machine neural networks (I-ELM-NNs). Among the nice properties of the proposed algorithm is that it significantly outperforms the algorithms proposed by Kosmatopoulos as well as other existing adaptive optimization algorithms. Moreover, contrary to the algorithms proposed by Kosmatopoulos , the proposed algorithm can operate efficiently in the case where the exogenous system inputs (e.g., disturbances, commands, demand, etc.) are unbounded signals.
Recently, we introduced an adaptive control design for linearly parameterized multi-input nonlinear systems admitting a known control Lyapunov function (CLF) that depends on the unknown system parameters. The main advantage of that design is that it overcomes the problem where the estimation model becomes uncontrollable (at regions of the state space where the actual system is controllable). However, the resulted adaptive control design is quite complicated and, moreover, it exhibited poor transient behavior in various applications. In this technical note, we propose and analyze a new computationally efficient adaptive control design that overcomes the aforementioned shortcomings. The proposed design is based on an adaptive optimization algorithm introduced recently by the author, which makes sure that the parameters to be optimized (which correspond to the controller parameters in this technical note) are modified so as to both lead to a decrease of the function to be minimized and satisfy a persistence of excitation condition. The main advantage of the proposed adaptive control design is that it can produce arbitrarily good transient performance outside (a) the regions of the state space where the actual system becomes uncontrollable and (b) a region of the parameter estimates space which shrinks exponentially fast. It is also worth noting that the class of systems where the proposed algorithm is applicable is more general than that of our previous work; however, it has to be emphasized that, due to the fact that the proposed algorithm involves the use of random sequences, all the established stability and convergence results are guaranteed to hold with probability one.
Conference abstracts and proceedings
Over the last years, an intensified interest has been shown in many studies for precision agriculture. Unmanned Aircraft Systems (UASs) are capable of solving a plethora of surveying tasks due to their flexibility, independence and customization. The incorporation of UASs remote sensing in precision agriculture enhances the abilities of crop mapping, management and identification through vegetation indices. In addition to this, different image analysis and computer vision processes were adopted trying to facilitate field operations in cooperation with human intervention to enhance the overall performance. In this paper, we present a practically oriented application on vineyards towards an integrated low-cost system which utilizes Spiral-STC (Spanning Tree Coverage) algorithm as a Coverage Path Planning (CPP) method. Based on the resulted flight campaign, UAV images were collected, and the incorporated image analysis processes finally extract vegetation knowledge. Also, geo referenced orthophotos and computer vision applications complete the generated oversight of the field. These supportive tools provide farmers with useful information (crop health indicators, weather predictions) letting them extrapolate knowledge and identify crop irregularities.
The rapid maturity of everyday sensor technologies has had a significant impact on our ability to collect information from the physical world. There are tremendous opportunities in using sensor technologies (both wired and wireless) for building operation, monitoring and control. The key promise of sensor technology in building operation is to reduce the cost of installing data acquisition and control systems (typically 40% of the cost of controls technology in a heating, ventilation, and air conditioning (HVAC) system). Reducing or eliminating this cost component has a dramatic effect on the overall installed system cost. With low-cost sensor and control systems, not only will the cost of system installation be significantly reduced, but it will become economical to use more sensors, thereby establishing highly energy efficient building operations and demand responsiveness that will enhance our electric grid reliability.
The introduction of Unmanned vehicles (UxVs) in the recent years has created a new security field that can use them as both a potential threat as well as new technological weapons against those threats. Dealing with these issues from the counter-threat perspective, the proposed architecture project focuses on designing and developing a complete system which utilizes the capabilities of multiple UxVs for surveillance objectives in different operational environments. Utilizing a combination of diverse UxVs equipped with various sensors, the developed architecture involves the detection and the characterization of threats based on both visual and thermal data. The identification of objects is enriched with additional information extracted from other sensors such as radars and RF sensors to secure the efficiency of the overall system. The current prototype displays diverse interoperability concerning the multiple visual sources that feed the system with the required optical data. Novel detection models identify the necessary threats while this information is enriched with higher-level semantic representations. Finally, the operator is informed properly according to the visual identification modules and the outcomes of the UxVs operations. The system can provide optimal surveillance capacities to the relevant authorities towards an increased situational awareness.
Several disastrous incidents from earthquakes have been recorded in recent and past human life. Despite the improvements in structural stability and vibration resilience, heavy structures still suffer from construction cost problems which usually hinder their potential investment. Vibration active control techniques present a great potential for reducing the anti-seismic protection costs. So far, existing building vibration control strategies are unable to provide a reliable operation able to reject the evolving uncertain non-linear dynamics that grow as the amplitude of the exogenous ground disturbance increases. This paper applies a vibration active control optimization methodology in applications involving large structures. A simulation model of the structure is used to optimize, in an offline manner, the total structure displacement metric. Simulation experiments demonstrate that the adopted approach namely Automated Fine-Tuning Cognitive Adaptive Optimization (AFT-CAO) – can effectively deal with seismic dynamics both in low and large seismic cases. AFT-CAO was proven capable to provide efficient control decisions that well-established LQR cannot outperform. The structure model used for the simulation tests consists by three vertically interconnected masses, each connected to an external lateral spring with an adjustable applied restoring force.
Further deterioration of the already burdened traffic conditions is expected within the following years, especially in high population density urban regions. To cope with such problem, centralized and decentralized adaptive optimization techniques have already been proposed in literature; introducing inefficient performance though, due to the highly stochastic dynamics involved, scaling and/or model unavailability problems, as well as data transmission limitations. To confront such problems, L4GCAO, a novel, model-free, decentralized, adaptive optimization approach, has been developed for maximizing the system’s overall performance, by calibrating the parameters of a given signal control strategy through decentralized self-learning elements (agents). This paper considers a realistic simulation scenario where the parameters of a signal control strategy applied at each network intersection are calibrated, to study the performance of L4GCAO. For comparison purposes, the thoroughly evaluated and verified centralized optimization counterpart approach of L4GCAO namely CAO – has also been adopted herein. The results of the study indicate that both CAO and L4GCAO present quite similar potential for improving the overall performance metric considered, with respect to a well-designed fixed time control strategy used as reference point.
This research develops a new on-line trajectory planning algorithm for a team of Autonomous Underwater Vehicles (AUVs). The goal of the AUVs is to cooperatively explore and map the ocean seafloor. As the morphology of the seabed is unknown and complex, standard non-convex algorithms perform insufficiently. To tackle this, a new simulation-based approach is proposed and numerically evaluated. This approach adapts the Parametrized Cognitive-based Adaptive Optimization (PCAO) algorithm. The algorithm transforms the exploration problem to a parametrized decision-making mechanism whose real-time implementation is feasible. Upon that transformation, this scheme calculates off-line a set of decision making mechanism’s parameters that approximate the – non-practically feasible – optimal solution. The advantages of the algorithm are significant computational simplicity, scalability, and the fact that it can straightforwardly embed any type of physical constraints and system limitations. In order to train the PCAO controller, two morphologically different seafloors are used. During this training, the algorithm outperforms an unrealistic optimal-one-step-ahead search algorithm. To demonstrate the universality of the controller, the most effective controller is used to map three new morphologically different seafloors. During the latter mapping experiment, the PCAO algorithm outperforms several gradient-descent-like approaches.
This paper deals with the problem of retrieving the optimal path between two points inside an unknown environment, utilizing a robot-scouter. The vast majority of the path planning frameworks for an unknown environment focuses on the problem of navigating a robot, as soon as possible, towards a pre-specified location. As a result, the final followed path between the start and end location is not necessarily the optimal one, as the objective of the robot at each timestamp is to minimize its current distance to the desirable location. However, there are several real-life applications, like the one formulated in this paper, where the robot-scouter has to find the minimum path between two positions in an unknown environment, which is going to be used in a future phase. In principle, the optimal path can be guaranteed by a searching agent that adopts an A*-like decision mechanism. In this paper, we propose a specifically-tailored variation (CIA*) of the A* algorithm to the problem in hand. CIA* inherits the A* optimality and efficiency guarantees, while at the same time exploits the learnt formation of the obstacles, to on-line revise the heuristic evaluation of the candidate states. As reported in the simulation results, CIA* achieves an enhancement in the range of 20-50%, over the typical A*, on the cells that have to be visited to guarantee the optimal path construction. An open-source implementation of the proposed algorithm along with a Matlab GUI are available 1 .
Within the current document a model independent, cognitive and adaptive optimization mechanism, namely CAO, is adopted for providing efficient speed/torque control actions. However since real-life tests were not feasible, a simulation model of a 1.4lt displacement gasoline car, playing the role of the actual car, was adopted while the potential maximum cruising speed levels are chosen so as to emulate a usual suburban route and the vehicle speed control is replicated by a common PID scheme. The control decisions are applied directly to the car throttle/torque pedal itself. The goal of the optimization application was to minimize the vehicle velocity error, while minimizing the total fuel consumption for a certain 10km trip with varying road angles/slopes. It should be noted that CAO module could be applied directly to a car system in a straightforward manner without any preparatory investigations. Initially PID gain values were selected arbitrary while a PID gain – tuning module from Matlab/Simulink was used in order to fine-tune them under certain road conditions. Finally the tuned values were used as initial ones for all CAO’s application cases (A, B, C and D). CAO presented substantial improvements in the specified performance index, with respect to the base case speed strategy, as well as to the PID tuning in all simulation scenarios considered.
In the current state of the art load management
and demand response actions in smart buildings are often
predetermined by a field engineer to a fixed set of (rule-based)
options. This fixed set of options often neglects the cyberphysical
nature of the building dynamics, thermostatic action
and building automation system. In this work we will combine a
rule-based load management program with a learning feedback
load management program that can operate on top of the
rules. We demonstrate via extensive simulations the effectiveness
of the program for intelligent management of the heating,
ventilating and air conditioning (HVAC) loads so as to exploit
renewable energy sources, while taking into account humanrelated
constraints like thermal comfort.
This paper proposes a Cognitive Stochastic Approximation (CSA)-based optimization method for charging an EV (electric vehicle) fleet, using a single, aggregate battery model. The charging station can either utilize the batteries of the parked vehicles to charge the vehicles before they leave, or can use power from the grid. The objective is to optimize the charging task with minimum energy costs, possibly taking into account price variations in the electricity price. The main advantage of the proposed approach is that it provides a nearly to optimal solution in the presence of uncertain charging/discharging dynamics. The method is evaluated through a numerical model of a grid-connected charging station. Four scenarios with different electricity price models are studied. The CSA optimization results are compared with the results obtained by a rule-based charging algorithm and by an open-loop optimal control algorithm: the results illustrate the advantages of the proposed CSA algorithm in minimizing the charging cost, satisfying the aggregate battery charge sustaining conditions and providing robust solutions in the presence of time-varying vehicle schedules.
The objective of this research is to evaluate the performance of a system of systems optimization algorithm, namely, L4G-PCAO, in building energy systems. Since the test bed of this research is an office building with more than two hundred occupiers, the heating and cooling demands of the building must always be fully satisfied. Consequently, changes in the currently-installed control system cannot be made forthrightly. Therefore, fresh ideas like implementation of new control strategies or optimization algorithms should be firstly put to the test via dynamic simulation, which makes engineers capable of examining new control and optimization strategies. The performance should then be analyzed and evaluated before implementing in the use case. This paper presents a strategy for simulative-based implementation of L4G-PCAO in a building energy system and also evaluates its performance. The results show that it is not only possible to conserve energy by applying this newly-developed optimization algorithm to existing control systems, but also it can shift the usage of energy sources in a more environment-friendly direction.
Microgrids equipped with small-scale renewable energy generation systems and energy storage units offer challenging opportunity from a control point of view. In fact, in order to improve resilience and enable islanded mode, microgrid
energy management systems must dynamically manage controllable loads by considering not only matching energy generation and consumption, but also thermal comfort of the occupants. Thermal comfort, which is often neglected or
oversimplified, plays a major role in dynamic demand response, especially in front of intermittent behavior of the renewable energy sources. This paper presents a novel control algorithm for joint demand response management and thermal comfort optimization in a microgrid composed of a block of buildings,
a photovoltaic array, a wind turbine, and an energy storage unit. In order to address the large-scale nature of the problem, the proposed control strategy adopt a two-level supervisory strategy: at the lower level, each building employs a local controller that processes only local measurements; at the upper
level, a centralized unit supervises and updates the three controllers with the aim of minimizing the aggregate energy cost and thermal discomfort of the microgrid. Comparisons with alternative strategies reveal that the proposed supervisory strategy efficiently manages the demand response so as to sensibly improve independence of the microgrid with respect to the main grid, and guarantees at the same time thermal comfort of the occupants.
The improvement of traffic flow in cities, traditionally involves signal control strategies of a centralized logic. In this context, several methodological approaches have been proposed and used. These approaches have been proven quite efficient in cases where elaborate models are available and regularly updated, information from all corners of the system can be collected and managed in a central manner, and the scale of the resulting control problem is medium to small. Otherwise, they may fail to respond efficiently to the prevailing traffic conditions; thus stipulating the necessity of developing techniques able to handle efficiently large-scale control problems with steadily evolving and highly complex, nonlinear and stochastic dynamics. This is the main reason for the recently observed shift towards the development of scalable approaches, which, though based on a decentralized structure (by junction), will be capable to improve the traffic flow efficiency at network level with low design effort and control infrastructure investment. Such an approach is presented in this paper along with some preliminary microscopic simulation results and future plans.
Over the recent past years research effort has been dedicated towards addressing a generic solution in System of Systems (SoS) control problems. Two are the main obstacles that have to be bypassed in such problem cases, especially in real life applications, rendering the optimization problem into a complicated/challenging one: (i) modelling/simulation tools are usually used in order to construct an as-close-as-possible to reality accurate model, whose construction though requires a considerable amount of time and effort and (ii) furthermore, standard control system designs when applied to SoS exhibit poor performance as they are required to handle very high-dimensional problems. In this paper, we present a first attempt towards addressing these issues. More precisely, a new adaptive optimal control methodology is presented and evaluated. The main attributes of the proposed control methodology is its local nature with minimum requirements for coordination between the constituent system of the SoS and its model-free nature.
The demand-side energy management of microgrids comprising of buildings of heterogeneous nature (residential, commercial, industrial, etc.) and thus exhibiting heterogeneous occupancy pattern, requires the development of appropriate energy management systems (EMSs) that can integrate the maximum exploitation of the distributed energy resources like photovoltaic panels with the thermal comfort of the occupants. This paper presents a simulation-based optimization approach for the design of an EMS in grid-connected photovoltaic-equipped microgrids with heterogeneous buildings and occupancy schedules. The EMS optimizes a multi-objective criterion that takes into account both the energy cost and the thermal comfort of the aggregate microgrid. A three-building microgrid test case is used to demonstrate the effectiveness of the proposed approach: comparisons with alternative rule-based and optimization-based EMSs show that the proposed EMS strategy exploits the occupancy information to automatically change the energy demand of each building, resulting in improved energy cost and thermal comfort.
Suitable control measures and strategies must be taken to counteract the reduced throughput and the degradation of the network infrastructure caused by traffic congestion in urban networks. This paper studies and analyzes the performance of an adaptive traffic-responsive strategy that manages the traffic light parameters (the cycle time and the split time) in an urban network to reduce traffic congestion. The proposed traffic-responsive strategy adopts a nearly-optimal control formulation: first, an (approximate) solution of the HJB is parametrized via an appropriate Lyapunov positive definite matrix; then, the solution is updated via a procedure that generates candidate control strategies and selects at each iteration the best one based on the estimation of close-to-optimality and the information coming from the simulation model of the network (simulation-based design). Simulation results obtained using an AIMSUN model of the traffic network of Chania, Greece, an urban traffic network containing many varieties of junction staging, demonstrate the efficiency of the proposed approach.
Within the project NOPTILUS, a fully functional system/methodology had been developed that allows the cooperative, fully-autonomous navigation of teams of AUVs when deployed in Static or Dynamic Underwater Map Construction (SDUMC) or Dynamic Underwater Phenomena Tracking (DUPT) missions. The key ingredient of this fully functional system/methodology (called the NOPTILUS Planning, Assignment and Navigation Module – NOPTILUS PAN) is an optimal control algorithm – called Parametrized Cognitive Adaptive Optmization – (PCAO) – developed by one of the NOPTILUS partners (CERTH). PCAO is firstly tailored and modified so as to be applicable to the problem of autonomous navigation of teams of AUVs when deployed in SDUMC or DUPT missions. For this purpose, a nonlinear model is developed so as to capture the dynamics of the AUVs, their sensors and the underwater environment. More precisely, the original PCAO-based approach is revised so as to be able to efficiently handle information coming from the localization module, the underwater acoustic communication module, the situation understanding module as well as instructions from the operator. The information coming from these modules is handled by the NOPTILUS PAN module without the need to enter in tedious re-design tasks. Two real-life experiments (involving teams of AUVs deployed in static mapping or simultaneous static mapping and dynamic target taking) demonstrate the efficiency and practicability of the NOPTILUS PAN module.
In this paper the experimental results coming from the application of a novel Building Optimization and Control (BOC) technique in two different large-scale buildings are exposed. This novel BOC technique, described in a previous companion paper (part I: optimization algorithms) and denominated as PCAO, shows (i) its ability to provide extremely fast and in a “plug-n-play” fashion an efficient BOC system no matter how large-scale and complex is the building and (ii) its ability to rapidly and efficiently adapt and calibrate itself so as to “quickly learn the best BOC policy” even in the case where a poor or no model for the building dynamics is available. It has to be emphasized that the two different buildings where the PCAO approach is applied and evaluated correspond to “difficult to control buildings”, i.e., efficient BOC system design for these two buildings is significantly more complex than that of an “average building”.
Building Optimization and Control (BOC) deals with the deployment of active techniques, aiming at efficiently controlling the active elements of a building (HVACs, concrete activation elements, etc.): these techniques can result in tremendous energy savings, without the need for passive expensive solutions (new materials for passive insulation, the use of glass facades etc.). In this paper, we present a new approach for BOC system design – abbreviated as PCAO – with the following attributes: (i) providing extremely fast and in a “plug-n-play” fashion an efficient BOC system no matter how large-scale and complex the building is (ii) rapidly and efficiently adapting and calibrating itself so as to “quickly learn the best BOC policy” even in the case where a poor or no model for the building dynamics is available. This paper mainly deals with the algorithmic details of the proposed approach: real-life results coming from the application of PCAO in two large scale buildings are presented in a companion paper (part II: real-life experimental results).
Recently, there has been a growing interest towards simulation-based control design (co-simulation), where the controller utilizes an optimizer to minimize or maximize an objective function (system performance) whose evaluation involves simulation of the system to be controlled. However, existing simulation-based approaches are not able to handle in a computationally efficient way large-scale optimization problems involving hundreds or thousands of states and parameters. In this paper, we propose and analyze a new simulation-based control design approach, employing an adaptive optimization algorithm capable of efficiently handle large-scale control problems. The convergence properties of the proposed algorithm are established. Simulation results exhibit efficiency of the proposed approach when applied to large-scale problems. The simulation results employ two large-scale real-life systems for which conventional popular optimization techniques totally fail to provide an efficient simulation-based control design.
Many Control Design approaches presuppose the knowledge of a state space description of the system. As in many real and large scale systems the construction of a state space model using analytical equations and rough assumptions is a time consuming process leading most of the times to questionable precision. System simulation software has been developed for several applications over the recent past decades. For this purpose a System Identification approach is proposed in the present paper. The idea of the System Identification from a simulation model using statistical measurements from the simulation output data has been of great interested for optimal control schemes which require accurate and furthermore, agile, simple and practical models to handle. In practice real systems and accurate simulation models, show nonlinear dynamic behavior which concludes to linear approximation-identification models being unsuitable for this purpose. The proposed Multi-Linear State Space Model Identification process is using the idea of mixing signals as weighted contribution factors for the final model behavior. A common Least Square error minimization process is used to minimize the model approximation error.
In this paper we present a solution to the problem of positioning a team of Micro Aerial Vehicles for a surveillance task in an environment of arbitrary and unknown morphology. The problem is addressed taking into account physical and environmental constraints like limited sensor capabilities and obstacle avoidance. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. The proposed method is a distributed extension of our previous work based on the Cognitive Adaptive Optimization (CAO) algorithm. This distributed and scalable approach allows us to obtain coordinated and safe trajectories to accomplish the task in 3D environments. The different formulation of the problem considered in this paper allows also dealing with communication constraints. We provide extensive experimental results using data collected by a team of aerial robots and compare the efficiency of the distributed and centralized approach.
In this paper, we present a new approach that is able to efficiently and fully-autonomously navigate a team of Unmanned Aerial or Underwater Vehicles (UAUV’s) when deployed in exploration of unknown static and dynamic environments towards providing accurate static/dynamic maps of the environment. Additionally to achieving to efficiently and fully-autonomously navigate the UAUV team, the proposed approach possesses certain advantages such as its extremely computational simplicity and scalability, and the fact that it can very straightforwardly embed and type of physical or other constraints and limitations (e.g., obstacle avoidance, nonlinear sensor noise models, localization fading environments, etc).
M. Achtelik, M. Achtelik, Y. Brunet, M. Chli, S. A. Chatzichristofis, J.D. Decotignie, K.M. Doth, F. Fraundorfer, L. Kneip, D. Gurdan, L. Heng, E. Kosmatopoulos, L. Doitsidis, G.H. Lee, S. Lynen, A. Martinelli, L. Meier, M. Pollefeys, D. Piguet, A. Renzaglia, D. Scaramuzza, R. Siegwart, J. Stumpf, P. Tanskanen, C. Troiani and S. Weiss, “SFly: Swarm of Micro Flying Robots”,in proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012), Vilamura, Algarve, Portugal, October 7 – October 12, 2012. (finalist for the best video award)
Current multi-AUV systems are far from being capable of fully autonomously taking over real-life complex situation-awareness operations. As such operations require advanced reasoning and decision-making abilities, current designs have to heavily rely on human operators. The involvement of humans, however, is by no means a guarantee of performance; humans can easily be over-whelmed by the information overload, fatigue can act detrimentally to their performance, properly coordinating vehicles actions is hard, and continuous operation is all but impossible. Within the European funded project NOPTILUS we take the view that an effective fully-autonomous multi-AUV concept/system, is capable of overcoming these shortcomings, by replacing human-operated operations by a fully autonomous one. In this paper, we present a new approach that is able to efficiently and fully-autonomously navigate a team of AUVs when deployed in exploration of unknown static and dynamic environments towards providing accurate static/dynamic maps of the environment. Additionally to achieving to efficiently and fully-autonomously navigate the AUV team, the proposed approach possesses certain advantages such as its extremely computational simplicity and scalability, and the fact that it can very straightforwardly embed and type of physical or other constraints and limitations (e.g., obstacle avoidance, nonlinear sensor noise models, localization fading environments, etc).
Book chapters
Attaining energy-efficiency in microgrids, a localized grouping of controllable loads with distributed energy resources, requires the development of appropriate feedback-based demand management systems (DMS) with the capability of controlling thermostatically controlled loads at the district level, so as to optimize the aggregate energy demand of the microgrid. Since the energy demand is mainly driven by human needs (i.e., human-in-the-loop thermal comfort) and weather conditions, the DMS feedback nature is necessary to exploit occupancy and weather information that might change in real-time as the microgrid is operating. In this work, we aim at creating a distributed DMS (D-DMS) whose crucial characteristics are: the capability of augmenting any rule-based DMS with a feedback action that improves performance in changing (weather or occupancy) conditions; a distributed intelligence monitoring logic to scale up the benefits of single-building DMS (S-DMS) up to district-level microgrids. An original test case, developed in EnergyPlus and composed of a microgrid district with 100 buildings with thermostatically controlled loads to be managed, is presented to assess the performance of the proposed strategy.
As systems continue to evolve they rely less on human decision-making and more on computational intelligence. This trend in conjunction to the available technologies for providing advanced sensing, measurement, process control, and communication lead towards the new field of Internet-of-Things (IoT). IoT systems are expected to play a major role in the design and development of future engineering platforms with new capabilities that far exceed today’s levels of autonomy, functionality, and usability. Although these systems exhibit remarkable characteristics, their design and implementation is a challenging issue, as numerous (heterogeneous) components and services have to be appropriately designed. The problem of designing efficient IoT becomes far more challenging in case the target system has to meet also timing constraints. This chapter discusses an advanced framework for implementing decision-making mechanisms for large-scale IoT platforms. In order to depict the efficiency of introduced framework, it was applied to customize the building’s cooling and heating in a smart-grid environment. For this purpose, a number of connected smart thermostats are employed, which should facilitate intelligent control to fulfill occupants’ needs, such as the energy consumption and the comfort level in a building environment. Towards this direction, appropriate mechanisms that enable smart thermostats to have the capability to monitor their own performance, to classify, to learn, and to take proper actions, were developed in a systematic way. Experimentation with various configuration setups highlights the superior of introduced solution compared to static temperature values, as well as existing control techniques. Additionally, the significant low computational complexity enables the sufficient implementation of this mechanism as part of a low-cost embedded system, which can be integrated into existing smart thermostats.
Nowadays, most energy savings and thermal comfort improvements in buildings rely on passive design and construction techniques like superinsulation, passive solar heat gain and shading. Despite the benefits of passive design, active techniques for Building Optimization and Control (BOC) can result in relevant energy savings in everyday buildings without the need for expensive investments associated with passive solutions. The essence of BOC algorithms lies in controlling the Heating, Ventilation, and Air Conditioning (HVAC) system of a building to manage energy demands and improve thermal comfort conditions for occupants. In order to develop efficient BOC systems two aspects that must be handled efficiently are the large-scale nonlinear nature of the optimization problem and the uncertainty in the dynamics of the building. This chapter addresses these two aspects by proposing an adaptive optimization approach aiming at adaptively solving the optimal energy efficient control building problem. Real-life experimental results coming from the application of the proposed control approach in two different office buildings are presented. The two buildings differ for climate zone (Mediterranean vs Continental), HVAC system (air conditioners vs radiant slabs), seasonal climate control problem (cooling vs heating) and construction techniques (poor thermal insulation vs well insulated building). The proposed optimization strategy is able to handle the large-scale nature of the optimization problem, taking advantage of the peculiar characteristics of each building, and self-tuning so as achieve significant improvements in both in energy savings and thermal comfort.
Other publications
More does not always mean better, L4GPCAO is another paradigm where with less resources more overall performance can be achieved when a group of synergetic agents is coordinated in a sufficient manner with just a hint!