This paper discusses the design and software-in-theloop implementation of adaptive formation cont... more This paper discusses the design and software-in-theloop implementation of adaptive formation controllers for fixedwing unmanned aerial vehicles (UAVs) with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission (e.g. depending on the payload). Modeling and autopilot design for such autonomous fixed-wing UAVs are presented. The modeling is implemented in Matlab, while the autopilot is based on Ardu-Pilot, a popular open-source autopilot suite. Specifically, the Ar-duPilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-inthe-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.
In recent years, algorithmic-based market manipulation in stock and power markets has considerabl... more In recent years, algorithmic-based market manipulation in stock and power markets has considerably increased, and it is difficult to identify all such manipulation cases. This causes serious challenges for market regulators. This work highlights and lists various aspects of the monitoring of stock and power markets, using as test cases the regulatory agencies and regulatory policies in diverse regions, including Hong Kong, the United Kingdom, the United States and the European Union. Reported cases of market manipulations in the regions are examined. In order to help establish a relevant digital regulatory system, this work reviews and categorizes the indicators used to monitor the stock and power markets, and provides an in-depth analysis of the relationship between the indicators and market manipulation. This study specifically compiles a set of 10 indicators for detecting manipulation in the stock market, utilizing the perspectives of return rate, liquidity, volatility, market se...
This paper studies the leaderless consensus problem of heterogeneous multiple networked Euler-Lag... more This paper studies the leaderless consensus problem of heterogeneous multiple networked Euler-Lagrange systems subject to persistent disturbances with unknown constant biases, amplitudes, initial phases and frequencies. The main characteristic of this study is that none of the agents has information of a common reference model or of a common reference trajectory. Therefore, the agents must simultaneously and in a distributed way: achieve consensus to a common reference model (group model); achieve consensus to a common reference trajectory; and reject the unknown disturbances. We show that this is possible via a suitable combination of techniques of distributed 'observers', internal model principle and adaptive regulation. The proposed design generalizes recent results on group model learning, which have been studied for linear agents over undirected networks. In this work, group model learning is achieved for Euler-Lagrange dynamics over directed networks in the presence of persistent unknown disturbances.
A distributed controller is designed for robust global phase synchronization of a network of unce... more A distributed controller is designed for robust global phase synchronization of a network of uncertain second-order Kuramoto oscillators with a leader system, modeled as a nonlinear autonomous exosystem. The phase angles being elements of the unit circle, we propose an adaptive hybrid strategy based on a hysteresis mechanism to obtain global results despite the well-known topological obstructions. Only an upper bound on the unknown parameters of the oscillators is required to keep the adaptive estimates in a compact set. Since the reference signal is not available to each network node, we design a distributed observer of the leader exosystem. Leveraging the results of hybrid systems theory, including reduction theorems, Lyapunov techniques, and properties of ω-limit sets, we prove robust global asymptotic stability of the closed-loop dynamics, despite the presence of an adaptive control law
The Energy Management System (EMS) is an efficient technique to monitor, control and enhance the ... more The Energy Management System (EMS) is an efficient technique to monitor, control and enhance the building performance. In the state-of-the-art, building performance analysis is separated into building simulation and control management: this may cause inaccuracies and extra operating time. Thus, a coherent framework to integrate building physics with various energy technologies and energy control management methods is highly required. This framework should be formed by simplified but accurate models of building physics and building energy technologies, and should allow for the selection of proper control strategies according to the control objectives and scenarios. Therefore, this paper reviews the fundamental mathematical modeling and control strategies to create such a framework. The mathematical models of (i) building physics and (ii) popular building energy technologies (renewable energy systems, common heating and cooling energy systems and energy distribution systems) are first...
IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, 2020
This work studies a new reinforcement learning method in the framework of Recursive Least-Squares... more This work studies a new reinforcement learning method in the framework of Recursive Least-Squares Temporal Difference (RLS-TD). Differently from the standard mechanism of eligibility traces, leading to RLS-TD(λ), in this work we show that the forgetting factor commonly used in gradientbased estimation has a similar role to the mechanism of eligibility traces. We adopt an instrumental variable perspective to illustrate this point and we propose a new algorithm, namely-RLS-TD with forgetting factor (RLS-TD-f). We test the proposed algorithm in a Policy Iteration setting, i.e. when the performance of an initially stabilizing controller must be improved. We take the cart-pole benchmark as experimental platform: extensive experiments show that the proposed RLS-TD algorithm exhibits larger performance improvements in the largest portion of the state space.
2021 60th IEEE Conference on Decision and Control (CDC), 2021
This work proposes a distributed control strategy for the robust global leader-follower phase syn... more This work proposes a distributed control strategy for the robust global leader-follower phase synchronization of Kuramoto oscillators with inertia. For a convenient design, the phase angles are represented as elements of the unit circle. In particular, we exploit a "half-angle" representation inspired by unit quaternions. The ensuing non-Euclidean state space poses some challenges for robust global stabilization, which can be conveniently overcome with dynamic hybrid feedback. For this reason, we propose a hybrid solution obtained by combining a distributed observer with local hysteresis-based tracking controllers. The overall closed-loop system is analyzed through reduction theorems and Lyapunov-based arguments.
2021 20th International Conference on Advanced Robotics (ICAR), 2021
Long standing challenges in adaptive bipedal walking control (i.e. control taking care of unknown... more Long standing challenges in adaptive bipedal walking control (i.e. control taking care of unknown robot parameters) were to unify the control design instead of designing multiple controllers for different walking phases as well as to bypass computing constraint forces, since it often leads to complex designs. A few attempts to design a single controller for all walking phases ignored or oversimplified the constraint forces. However, these forces are state-dependent and may lead to conservative performance or instability if not countered properly. This work proposes an innovative adaptive control method, based on artificial time delay control, which covers the entire bipedal walking phase and provides robustness against state-dependent unmodelled dynamics such as constraint forces and external impulsive forces arising during walking. Studies using a high fidelity simulator under various forms of disturbances show the effectiveness of the proposed design over the state of the art.
Planning and reinforcement learning are two key approaches to sequential decision making. Multi-s... more Planning and reinforcement learning are two key approaches to sequential decision making. Multi-step approximate real-time dynamic programming, a recently successful algorithm class of which AlphaZero [Silver et al., 2018] is an example, combines both by nesting planning within a learning loop. However, the combination of planning and learning introduces a new question: how should we balance time spend on planning, learning and acting? The importance of this trade-off has not been explicitly studied before. We show that it is actually of key importance, with computational results indicating that we should neither plan too long nor too short. Conceptually, we identify a new spectrum of planning-learning algorithms which ranges from exhaustive search (long planning) to model-free RL (no planning), with optimal performance achieved midway.
A botnet is a network of remotely-controlled infected computers that can send spam, spread viruse... more A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-service attacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadily increased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and more difficult to be detected, because they make use of Peer-to-Peer protocols (eMule, Torrent, Frostwire, Vuze, Skype and many others). To improve the detectability of botnet activities, this paper introduces the idea of association analysis in the field of data mining, and proposes a system to detect botnets based on the FP-growth (Frequent Pattern Tree) frequent item mining algorithm. The detection system is composed of three parts: packet collection processing, rule mining, and statistical analysis of rules. Its characteristic feature is the rule-based classification of different botnet behaviors in a fast and unsu...
Despite the progress in the field of longitudinal formations of automated vehicles, only recently... more Despite the progress in the field of longitudinal formations of automated vehicles, only recently an interpretation of longitudinal platooning has been given in the framework of disturbance decoupling, i.e. the problem of making a controlled output independent of a disturbance. The appealing feature of this interpretation is that the disturbance decoupling approach naturally yields a decentralized controller that guarantees stability and string stability. In this work, we further exploit the disturbance decoupling framework and we show that convergence to a stable, string stable and disturbance decoupled behavior can be achieved even in the presence of parametric uncertainty of the engine time constant. We refer to this framework as adaptive disturbance decoupling.
2016 IEEE 55th Conference on Decision and Control (CDC), 2016
This work proposes an iterative procedure for static output feedback of polynomial systems based ... more This work proposes an iterative procedure for static output feedback of polynomial systems based on Sum-of-Squares optimization. Necessary and sufficient conditions for static output feedback stabilization of polynomial systems are formulated, both for the global and for the local stabilization case. Since the proposed conditions are bilinear with respect to the decision variables, an iterative procedure is proposed for the solution of the stabilization problem. Every iteration is shown to improve the performance with respect to the previous one, even if convergence to a local minimum might occur. Since polynomial Lyapunov functions and control laws are considered, a Sum-of-Squares optimization approach is adopted. A numerical example illustrates the results.
Adaptive CACC strategies have been recently proposed to stabilize a platoon with non-identical an... more Adaptive CACC strategies have been recently proposed to stabilize a platoon with non-identical and uncertain vehicle dynamics (heterogeneous platoon). This work proposes a method to augment such strategies with a mechanism coping with saturation constraints (i.e. engine constraints). In fact, in a platoon of heterogeneous vehicles, engine constraints might lead to loss of cohesiveness. The proposed mechanism is based on making the reference dynamics (i.e the dynamics to which the platoon should homogenize) 'not too demanding', by applying a properly designed saturation action. Such saturation action will allow all vehicles in the platoon not to hit their engine bounds. Cohesiveness will then be achieved at the price of losing some performance, which is in line with the state of art studies on this topic. Simulations on a platoon of 5 vehicles are conducted to validate the theoretical analysis.
Condensing boilers achieve higher efficiency than traditional boilers by using waste heat in flue... more Condensing boilers achieve higher efficiency than traditional boilers by using waste heat in flue gases to preheat cold return water entering the boiler. Water vapor produced during combustion is condensed into liquid form, thus recovering its latent heat of vaporization, leading to around 10-12% increased efficiency. Many countries have encouraged the use of condensing boilers with financial incentives. It is thus important to develop software tools to assess the correct functioning of the boiler and eventually detect problems. Current monitoring tools are based on boiler static maps and on large sets of historical data, and are unable to assess timely loss of performance due to degradation of the efficiency curve or water leakages. This work develops a set of fault detection and diagnosis tools for dynamic energy efficiency monitoring and assessment in condensing boilers, i.e. performance degradation and faults can be detected using real-time measurements: this real-time feature is particularly relevant because of the limited amount of data that can be stored by state-of-the-art building energy management systems. The monitoring tools are organized as follows: a bimodal parameter estimator to detect deviations of the efficiency of the boiler from nominal values in both condensing and noncondensing mode; a virtual sensor for the estimation of the water mass flow rate; filters to detect actuator and sensor faults, possibly due to control and sensing problems. Most importantly, structural properties for detection and isolation of actuators and sensing faults are given: these properties are crucial to understand which faults can be diagnosed given the available measurements. The effectiveness of these tools is verified via extensive simulations.
This paper investigates the identification of continuous piecewise affine systems in state space ... more This paper investigates the identification of continuous piecewise affine systems in state space form with jointly unknown partition and subsystem matrices. The partition of the system is generated by the so-called centers. By representing continuous piecewise affine systems in the max-form and using a recursive Gauss-Newton algorithm for a suitable cost function, we derive adaptive laws to online estimate parameters including both subsystem matrices and centers. The effectiveness of the proposed approach is demonstrated with a numerical example.
An adaptive decentralized strategy for active queue management of TCP flows over communication ne... more An adaptive decentralized strategy for active queue management of TCP flows over communication networks is presented. The proposed strategy solves locally, at each link, an optimal control problem, minimizing a cost composed of residual capacity and buffer queue size. The solution of the optimal control problem exploits an adaptive optimization algorithm aiming at adaptively minimizing a suitable approximation of the Hamilton-Jacobi-Bellman equation associated with the optimal control problem. Simulations results, obtained by using a fluid flow based model of the communication network and a common network topology, show improvement with respect to the Random Early Detection strategy. Besides, it is shown that the performance of the proposed decentralized solution is comparable with the performance obtained with a centralized strategy, which solves the optimal control problem via a central unit that maintains the flow states of the entire network.
Takedown policy Please contact us and provide details if you believe this document breaches copyr... more Takedown policy Please contact us and provide details if you believe this document breaches copyrights. We will remove access to the work immediately and investigate your claim.
Adaptive mixing control (AMC) is a recently developed control scheme for uncertain plants, where ... more Adaptive mixing control (AMC) is a recently developed control scheme for uncertain plants, where the control action coming from a bank of precomputed controller is mixed based on the parameter estimates generated by an on-line parameter estimator. Even if the stability of the control scheme, also in the presence of modeling errors and disturbances, has been shown analytically, its transient performance might be sensitive to the initial conditions of the parameter estimator. In particular, for some initial conditions, transient oscillations may not be acceptable in practical applications. In order to account for such a possible phenomenon and to improve the learning capability of the adaptive scheme, in this paper a new mixing architecture is developed, involving the use of parallel parameter estimators, or multi-estimators, each one working on a small subset of the uncertainty set. A supervisory logic, using performance signals based on the past and present estimation error, selects the parameter estimate to determine the mixing of the controllers. The stability and robustness properties of the resulting approach, referred to as multi-estimator adaptive mixing control (Multi-AMC), are analytically established. Besides, extensive simulations demonstrate that the scheme improves the transient performance of the original AMC with a single estimator. The control scheme and the analysis are carried out in a discrete-time framework, for easier implementation of the method in digital control.
Using a setting in which the input is communicated among neighbors (without exchanging any distri... more Using a setting in which the input is communicated among neighbors (without exchanging any distributed observer variables), the problem of synchronizing an acyclic network of linear uncertain agents has been formulated recently as a distributed model reference adaptive control (MRAC) where each agent tries to converge to the model defined by its neighbors. In this work we show how to parametrize the distributed MRAC in cyclic and undirected graphs.
This paper discusses the design and software-in-theloop implementation of adaptive formation cont... more This paper discusses the design and software-in-theloop implementation of adaptive formation controllers for fixedwing unmanned aerial vehicles (UAVs) with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission (e.g. depending on the payload). Modeling and autopilot design for such autonomous fixed-wing UAVs are presented. The modeling is implemented in Matlab, while the autopilot is based on Ardu-Pilot, a popular open-source autopilot suite. Specifically, the Ar-duPilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-inthe-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.
In recent years, algorithmic-based market manipulation in stock and power markets has considerabl... more In recent years, algorithmic-based market manipulation in stock and power markets has considerably increased, and it is difficult to identify all such manipulation cases. This causes serious challenges for market regulators. This work highlights and lists various aspects of the monitoring of stock and power markets, using as test cases the regulatory agencies and regulatory policies in diverse regions, including Hong Kong, the United Kingdom, the United States and the European Union. Reported cases of market manipulations in the regions are examined. In order to help establish a relevant digital regulatory system, this work reviews and categorizes the indicators used to monitor the stock and power markets, and provides an in-depth analysis of the relationship between the indicators and market manipulation. This study specifically compiles a set of 10 indicators for detecting manipulation in the stock market, utilizing the perspectives of return rate, liquidity, volatility, market se...
This paper studies the leaderless consensus problem of heterogeneous multiple networked Euler-Lag... more This paper studies the leaderless consensus problem of heterogeneous multiple networked Euler-Lagrange systems subject to persistent disturbances with unknown constant biases, amplitudes, initial phases and frequencies. The main characteristic of this study is that none of the agents has information of a common reference model or of a common reference trajectory. Therefore, the agents must simultaneously and in a distributed way: achieve consensus to a common reference model (group model); achieve consensus to a common reference trajectory; and reject the unknown disturbances. We show that this is possible via a suitable combination of techniques of distributed 'observers', internal model principle and adaptive regulation. The proposed design generalizes recent results on group model learning, which have been studied for linear agents over undirected networks. In this work, group model learning is achieved for Euler-Lagrange dynamics over directed networks in the presence of persistent unknown disturbances.
A distributed controller is designed for robust global phase synchronization of a network of unce... more A distributed controller is designed for robust global phase synchronization of a network of uncertain second-order Kuramoto oscillators with a leader system, modeled as a nonlinear autonomous exosystem. The phase angles being elements of the unit circle, we propose an adaptive hybrid strategy based on a hysteresis mechanism to obtain global results despite the well-known topological obstructions. Only an upper bound on the unknown parameters of the oscillators is required to keep the adaptive estimates in a compact set. Since the reference signal is not available to each network node, we design a distributed observer of the leader exosystem. Leveraging the results of hybrid systems theory, including reduction theorems, Lyapunov techniques, and properties of ω-limit sets, we prove robust global asymptotic stability of the closed-loop dynamics, despite the presence of an adaptive control law
The Energy Management System (EMS) is an efficient technique to monitor, control and enhance the ... more The Energy Management System (EMS) is an efficient technique to monitor, control and enhance the building performance. In the state-of-the-art, building performance analysis is separated into building simulation and control management: this may cause inaccuracies and extra operating time. Thus, a coherent framework to integrate building physics with various energy technologies and energy control management methods is highly required. This framework should be formed by simplified but accurate models of building physics and building energy technologies, and should allow for the selection of proper control strategies according to the control objectives and scenarios. Therefore, this paper reviews the fundamental mathematical modeling and control strategies to create such a framework. The mathematical models of (i) building physics and (ii) popular building energy technologies (renewable energy systems, common heating and cooling energy systems and energy distribution systems) are first...
IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, 2020
This work studies a new reinforcement learning method in the framework of Recursive Least-Squares... more This work studies a new reinforcement learning method in the framework of Recursive Least-Squares Temporal Difference (RLS-TD). Differently from the standard mechanism of eligibility traces, leading to RLS-TD(λ), in this work we show that the forgetting factor commonly used in gradientbased estimation has a similar role to the mechanism of eligibility traces. We adopt an instrumental variable perspective to illustrate this point and we propose a new algorithm, namely-RLS-TD with forgetting factor (RLS-TD-f). We test the proposed algorithm in a Policy Iteration setting, i.e. when the performance of an initially stabilizing controller must be improved. We take the cart-pole benchmark as experimental platform: extensive experiments show that the proposed RLS-TD algorithm exhibits larger performance improvements in the largest portion of the state space.
2021 60th IEEE Conference on Decision and Control (CDC), 2021
This work proposes a distributed control strategy for the robust global leader-follower phase syn... more This work proposes a distributed control strategy for the robust global leader-follower phase synchronization of Kuramoto oscillators with inertia. For a convenient design, the phase angles are represented as elements of the unit circle. In particular, we exploit a "half-angle" representation inspired by unit quaternions. The ensuing non-Euclidean state space poses some challenges for robust global stabilization, which can be conveniently overcome with dynamic hybrid feedback. For this reason, we propose a hybrid solution obtained by combining a distributed observer with local hysteresis-based tracking controllers. The overall closed-loop system is analyzed through reduction theorems and Lyapunov-based arguments.
2021 20th International Conference on Advanced Robotics (ICAR), 2021
Long standing challenges in adaptive bipedal walking control (i.e. control taking care of unknown... more Long standing challenges in adaptive bipedal walking control (i.e. control taking care of unknown robot parameters) were to unify the control design instead of designing multiple controllers for different walking phases as well as to bypass computing constraint forces, since it often leads to complex designs. A few attempts to design a single controller for all walking phases ignored or oversimplified the constraint forces. However, these forces are state-dependent and may lead to conservative performance or instability if not countered properly. This work proposes an innovative adaptive control method, based on artificial time delay control, which covers the entire bipedal walking phase and provides robustness against state-dependent unmodelled dynamics such as constraint forces and external impulsive forces arising during walking. Studies using a high fidelity simulator under various forms of disturbances show the effectiveness of the proposed design over the state of the art.
Planning and reinforcement learning are two key approaches to sequential decision making. Multi-s... more Planning and reinforcement learning are two key approaches to sequential decision making. Multi-step approximate real-time dynamic programming, a recently successful algorithm class of which AlphaZero [Silver et al., 2018] is an example, combines both by nesting planning within a learning loop. However, the combination of planning and learning introduces a new question: how should we balance time spend on planning, learning and acting? The importance of this trade-off has not been explicitly studied before. We show that it is actually of key importance, with computational results indicating that we should neither plan too long nor too short. Conceptually, we identify a new spectrum of planning-learning algorithms which ranges from exhaustive search (long planning) to model-free RL (no planning), with optimal performance achieved midway.
A botnet is a network of remotely-controlled infected computers that can send spam, spread viruse... more A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-service attacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadily increased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and more difficult to be detected, because they make use of Peer-to-Peer protocols (eMule, Torrent, Frostwire, Vuze, Skype and many others). To improve the detectability of botnet activities, this paper introduces the idea of association analysis in the field of data mining, and proposes a system to detect botnets based on the FP-growth (Frequent Pattern Tree) frequent item mining algorithm. The detection system is composed of three parts: packet collection processing, rule mining, and statistical analysis of rules. Its characteristic feature is the rule-based classification of different botnet behaviors in a fast and unsu...
Despite the progress in the field of longitudinal formations of automated vehicles, only recently... more Despite the progress in the field of longitudinal formations of automated vehicles, only recently an interpretation of longitudinal platooning has been given in the framework of disturbance decoupling, i.e. the problem of making a controlled output independent of a disturbance. The appealing feature of this interpretation is that the disturbance decoupling approach naturally yields a decentralized controller that guarantees stability and string stability. In this work, we further exploit the disturbance decoupling framework and we show that convergence to a stable, string stable and disturbance decoupled behavior can be achieved even in the presence of parametric uncertainty of the engine time constant. We refer to this framework as adaptive disturbance decoupling.
2016 IEEE 55th Conference on Decision and Control (CDC), 2016
This work proposes an iterative procedure for static output feedback of polynomial systems based ... more This work proposes an iterative procedure for static output feedback of polynomial systems based on Sum-of-Squares optimization. Necessary and sufficient conditions for static output feedback stabilization of polynomial systems are formulated, both for the global and for the local stabilization case. Since the proposed conditions are bilinear with respect to the decision variables, an iterative procedure is proposed for the solution of the stabilization problem. Every iteration is shown to improve the performance with respect to the previous one, even if convergence to a local minimum might occur. Since polynomial Lyapunov functions and control laws are considered, a Sum-of-Squares optimization approach is adopted. A numerical example illustrates the results.
Adaptive CACC strategies have been recently proposed to stabilize a platoon with non-identical an... more Adaptive CACC strategies have been recently proposed to stabilize a platoon with non-identical and uncertain vehicle dynamics (heterogeneous platoon). This work proposes a method to augment such strategies with a mechanism coping with saturation constraints (i.e. engine constraints). In fact, in a platoon of heterogeneous vehicles, engine constraints might lead to loss of cohesiveness. The proposed mechanism is based on making the reference dynamics (i.e the dynamics to which the platoon should homogenize) 'not too demanding', by applying a properly designed saturation action. Such saturation action will allow all vehicles in the platoon not to hit their engine bounds. Cohesiveness will then be achieved at the price of losing some performance, which is in line with the state of art studies on this topic. Simulations on a platoon of 5 vehicles are conducted to validate the theoretical analysis.
Condensing boilers achieve higher efficiency than traditional boilers by using waste heat in flue... more Condensing boilers achieve higher efficiency than traditional boilers by using waste heat in flue gases to preheat cold return water entering the boiler. Water vapor produced during combustion is condensed into liquid form, thus recovering its latent heat of vaporization, leading to around 10-12% increased efficiency. Many countries have encouraged the use of condensing boilers with financial incentives. It is thus important to develop software tools to assess the correct functioning of the boiler and eventually detect problems. Current monitoring tools are based on boiler static maps and on large sets of historical data, and are unable to assess timely loss of performance due to degradation of the efficiency curve or water leakages. This work develops a set of fault detection and diagnosis tools for dynamic energy efficiency monitoring and assessment in condensing boilers, i.e. performance degradation and faults can be detected using real-time measurements: this real-time feature is particularly relevant because of the limited amount of data that can be stored by state-of-the-art building energy management systems. The monitoring tools are organized as follows: a bimodal parameter estimator to detect deviations of the efficiency of the boiler from nominal values in both condensing and noncondensing mode; a virtual sensor for the estimation of the water mass flow rate; filters to detect actuator and sensor faults, possibly due to control and sensing problems. Most importantly, structural properties for detection and isolation of actuators and sensing faults are given: these properties are crucial to understand which faults can be diagnosed given the available measurements. The effectiveness of these tools is verified via extensive simulations.
This paper investigates the identification of continuous piecewise affine systems in state space ... more This paper investigates the identification of continuous piecewise affine systems in state space form with jointly unknown partition and subsystem matrices. The partition of the system is generated by the so-called centers. By representing continuous piecewise affine systems in the max-form and using a recursive Gauss-Newton algorithm for a suitable cost function, we derive adaptive laws to online estimate parameters including both subsystem matrices and centers. The effectiveness of the proposed approach is demonstrated with a numerical example.
An adaptive decentralized strategy for active queue management of TCP flows over communication ne... more An adaptive decentralized strategy for active queue management of TCP flows over communication networks is presented. The proposed strategy solves locally, at each link, an optimal control problem, minimizing a cost composed of residual capacity and buffer queue size. The solution of the optimal control problem exploits an adaptive optimization algorithm aiming at adaptively minimizing a suitable approximation of the Hamilton-Jacobi-Bellman equation associated with the optimal control problem. Simulations results, obtained by using a fluid flow based model of the communication network and a common network topology, show improvement with respect to the Random Early Detection strategy. Besides, it is shown that the performance of the proposed decentralized solution is comparable with the performance obtained with a centralized strategy, which solves the optimal control problem via a central unit that maintains the flow states of the entire network.
Takedown policy Please contact us and provide details if you believe this document breaches copyr... more Takedown policy Please contact us and provide details if you believe this document breaches copyrights. We will remove access to the work immediately and investigate your claim.
Adaptive mixing control (AMC) is a recently developed control scheme for uncertain plants, where ... more Adaptive mixing control (AMC) is a recently developed control scheme for uncertain plants, where the control action coming from a bank of precomputed controller is mixed based on the parameter estimates generated by an on-line parameter estimator. Even if the stability of the control scheme, also in the presence of modeling errors and disturbances, has been shown analytically, its transient performance might be sensitive to the initial conditions of the parameter estimator. In particular, for some initial conditions, transient oscillations may not be acceptable in practical applications. In order to account for such a possible phenomenon and to improve the learning capability of the adaptive scheme, in this paper a new mixing architecture is developed, involving the use of parallel parameter estimators, or multi-estimators, each one working on a small subset of the uncertainty set. A supervisory logic, using performance signals based on the past and present estimation error, selects the parameter estimate to determine the mixing of the controllers. The stability and robustness properties of the resulting approach, referred to as multi-estimator adaptive mixing control (Multi-AMC), are analytically established. Besides, extensive simulations demonstrate that the scheme improves the transient performance of the original AMC with a single estimator. The control scheme and the analysis are carried out in a discrete-time framework, for easier implementation of the method in digital control.
Using a setting in which the input is communicated among neighbors (without exchanging any distri... more Using a setting in which the input is communicated among neighbors (without exchanging any distributed observer variables), the problem of synchronizing an acyclic network of linear uncertain agents has been formulated recently as a distributed model reference adaptive control (MRAC) where each agent tries to converge to the model defined by its neighbors. In this work we show how to parametrize the distributed MRAC in cyclic and undirected graphs.
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