Networked Control Systems with Intermittent Feedback
Networked Control Systems with Intermittent Feedback focuses on spatially distributed systems known as Networked Control Systems (NCSs), where communication between sensors, actuators, and controllers is executed through a shared (either wired or wireless) network. NCSs offer significant advantages, including reduced installation and maintenance costs and increased flexibility compared to traditional control systems that use dedicated point-to-point connections for information exchange.
The primary aim of this book is to present a coherent and versatile framework, suitable for a range of scenarios examined by the authors over several years. This framework is applicable to nonlinear time-varying dynamic plants and controllers with delayed dynamics. It also accommodates a broad class of scheduling protocols—static, dynamic, probabilistic, and priority-oriented—as well as delayed, noisy, lossy, and intermittent information exchange.
Moreover, the book addresses decentralized control problems involving heterogeneous agents with time-varying directed communication topologies that are not necessarily balanced. It covers both state- and output-feedback, off-line and on-line intermittent feedback, optimal intermittent feedback through Approximate Dynamic Programming (ADP) and Reinforcement Learning (RL), and control systems with exogenous disturbances and modelling uncertainties.
Networked Control Systems with Intermittent Feedback focuses on spatially distributed systems known as Networked Control Systems (NCSs), where communication between sensors, actuators, and controllers is executed through a shared (either wired or wireless) network. NCSs offer significant advantages, including reduced installation and maintenance costs and increased flexibility compared to traditional control systems that use dedicated point-to-point connections for information exchange.
The primary aim of this book is to present a coherent and versatile framework, suitable for a range of scenarios examined by the authors over several years. This framework is applicable to nonlinear time-varying dynamic plants and controllers with delayed dynamics. It also accommodates a broad class of scheduling protocols—static, dynamic, probabilistic, and priority-oriented—as well as delayed, noisy, lossy, and intermittent information exchange.
Moreover, the book addresses decentralized control problems involving heterogeneous agents with time-varying directed communication topologies that are not necessarily balanced. It covers both state- and output-feedback, off-line and on-line intermittent feedback, optimal intermittent feedback through Approximate Dynamic Programming (ADP) and Reinforcement Learning (RL), and control systems with exogenous disturbances and modelling uncertainties.
Description
Networked Control Systems with Intermittent Feedback focuses on spatially distributed systems known as Networked Control Systems (NCSs), where communication between sensors, actuators, and controllers is executed through a shared (either wired or wireless) network. NCSs offer significant advantages, including reduced installation and maintenance costs and increased flexibility compared to traditional control systems that use dedicated point-to-point connections for information exchange.
The primary aim of this book is to present a coherent and versatile framework, suitable for a range of scenarios examined by the authors over several years. This framework is applicable to nonlinear time-varying dynamic plants and controllers with delayed dynamics. It also accommodates a broad class of scheduling protocols—static, dynamic, probabilistic, and priority-oriented—as well as delayed, noisy, lossy, and intermittent information exchange.
Moreover, the book addresses decentralized control problems involving heterogeneous agents with time-varying directed communication topologies that are not necessarily balanced. It covers both state- and output-feedback, off-line and on-line intermittent feedback, optimal intermittent feedback through Approximate Dynamic Programming (ADP) and Reinforcement Learning (RL), and control systems with exogenous disturbances and modelling uncertainties.
