Sarangapani Jagannathan – författare

In the era of cyber-physical systems, the area of control of complex systems has grown to be one of the hardest in terms of algorithmic design techniques and analytical tools. The 23 chapters, written by international specialists in the field, cover a variety of interests within the broader field of learning, adaptation, optimization and networked control. The editors have grouped these into the following 5 sections: "Introduction and Background on Control Theory�, "Adaptive Control and Neuroscience�, "Adaptive Learning Algorithms�, "Cyber-Physical Systems and Cooperative Control�, "Applications�.The diversity of the research presented gives the reader a unique opportunity to explore a comprehensive overview of a field of great interest to control and system theorists.

This book is intended for researchers and control engineers in machine learning, adaptive control, optimization and automatic control systems, including Electrical Engineers, Computer Science Engineers, Mechanical Engineers, Aerospace/Automotive Engineers, and Industrial Engineers. It could be used as a text or reference for advanced courses in complex control systems.

. Collection of chapters from several well-known professors and researchers that will showcase their recent work

. Presents different state-of-the-art control approaches and theory for complex systems

. Gives algorithms that take into consideration the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals and malicious attacks compromising the security of networked teams

. Real system examples and figures throughout, make ideas concrete

Includes chapters from several well-known professors and researchers that showcases their recent work Presents different state-of-the-art control approaches and theory for complex systems Explores the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals, and malicious attacks compromising the security of networked teams Serves as a helpful reference for researchers and control engineers working with machine learning, adaptive control, and automatic control systems

In the era of cyber-physical systems, the area of control of complex systems has grown to be one of the hardest in terms of algorithmic design techniques and analytical tools. The 23 chapters, written by international specialists in the field, cover a variety of interests within the broader field of learning, adaptation, optimization and networked control. The editors have grouped these into the following 5 sections: "Introduction and Background on Control Theory, "Adaptive Control and Neuroscience, "Adaptive Learning Algorithms, "Cyber-Physical Systems and Cooperative Control, "Applications.The diversity of the research presented gives the reader a unique opportunity to explore a comprehensive overview of a field of great interest to control and system theorists.This book is intended for researchers and control engineers in machine learning, adaptive control, optimization and automatic control systems, including Electrical Engineers, Computer Science Engineers, Mechanical Engineers, Aerospace/Automotive Engineers, and Industrial Engineers. It could be used as a text or reference for advanced courses in complex control systems.• Collection of chapters from several well-known professors and researchers that will showcase their recent work• Presents different state-of-the-art control approaches and theory for complex systems• Gives algorithms that take into consideration the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals and malicious attacks compromising the security of networked teams• Real system examples and figures throughout, make ideas concrete- Includes chapters from several well-known professors and researchers that showcases their recent work- Presents different state-of-the-art control approaches and theory for complex systems- Explores the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals, and malicious attacks compromising the security of networked teams- Serves as a helpful reference for researchers and control engineers working with machine learning, adaptive control, and automatic control systems

Optimal Event-triggered Control using Adaptive Dynamic Programming discusses event triggered controller design which includes optimal control and event sampling design for linear and nonlinear dynamic systems including networked control systems (NCS) when the system dynamics are both known and uncertain. The NCS are a first step to realize cyber-physical systems (CPS) or industry 4.0 vision. The authors apply several powerful modern control techniques to the design of event-triggered controllers and derive event-trigger condition and demonstrate closed-loop stability. Detailed derivations, rigorous stability proofs, computer simulation examples, and downloadable MATLAB® codes are included for each case.The book begins by providing background on linear and nonlinear systems, NCS, networked imperfections, distributed systems, adaptive dynamic programming and optimal control, stability theory, and optimal adaptive event-triggered controller design in continuous-time and discrete-time for linear, nonlinear and distributed systems. It lays the foundation for reinforcement learning-based optimal adaptive controller use for infinite horizons. The text then:Introduces event triggered control of linear and nonlinear systems, describing the design of adaptive controllers for themPresents neural network-based optimal adaptive control and game theoretic formulation of linear and nonlinear systems enclosed by a communication networkAddresses the stochastic optimal control of linear and nonlinear NCS by using neuro dynamic programmingExplores optimal adaptive design for nonlinear two-player zero-sum games under communication constraints to solve optimal policy and event trigger conditionTreats an event-sampled distributed linear and nonlinear systems to minimize transmission of state and control signals within the feedback loop via the communication networkCovers several examples along the way and provides applications of event triggered control of robot manipulators, UAV and distributed joint optimal network scheduling and control design for wireless NCS/CPS in order to realize industry 4.0 visionAn ideal textbook for senior undergraduate students, graduate students, university researchers, and practicing engineers, Optimal Event Triggered Control Design using Adaptive Dynamic Programming instills a solid understanding of neural network-based optimal controllers under event-sampling and how to build them so as to attain CPS or Industry 4.0 vision.

Optimal Event-triggered Control using Adaptive Dynamic Programming discusses event triggered controller design which includes optimal control and event sampling design for linear and nonlinear dynamic systems including networked control systems (NCS) when the system dynamics are both known and uncertain. The NCS are a first step to realize cyber-physical systems (CPS) or industry 4.0 vision. The authors apply several powerful modern control techniques to the design of event-triggered controllers and derive event-trigger condition and demonstrate closed-loop stability. Detailed derivations, rigorous stability proofs, computer simulation examples, and downloadable MATLAB® codes are included for each case.The book begins by providing background on linear and nonlinear systems, NCS, networked imperfections, distributed systems, adaptive dynamic programming and optimal control, stability theory, and optimal adaptive event-triggered controller design in continuous-time and discrete-time for linear, nonlinear and distributed systems. It lays the foundation for reinforcement learning-based optimal adaptive controller use for infinite horizons. The text then:Introduces event triggered control of linear and nonlinear systems, describing the design of adaptive controllers for themPresents neural network-based optimal adaptive control and game theoretic formulation of linear and nonlinear systems enclosed by a communication networkAddresses the stochastic optimal control of linear and nonlinear NCS by using neuro dynamic programmingExplores optimal adaptive design for nonlinear two-player zero-sum games under communication constraints to solve optimal policy and event trigger conditionTreats an event-sampled distributed linear and nonlinear systems to minimize transmission of state and control signals within the feedback loop via the communication networkCovers several examples along the way and provides applications of event triggered control of robot manipulators, UAV and distributed joint optimal network scheduling and control design for wireless NCS/CPS in order to realize industry 4.0 visionAn ideal textbook for senior undergraduate students, graduate students, university researchers, and practicing engineers, Optimal Event Triggered Control Design using Adaptive Dynamic Programming instills a solid understanding of neural network-based optimal controllers under event-sampling and how to build them so as to attain CPS or Industry 4.0 vision.

Optimal Event-triggered Control using Adaptive Dynamic Programming discusses event triggered controller design which includes optimal control and event sampling design for linear and nonlinear dynamic systems including networked control systems (NCS) when the system dynamics are both known and uncertain. The NCS are a first step to realize cyber-physical systems (CPS) or industry 4.0 vision. The authors apply several powerful modern control techniques to the design of event-triggered controllers and derive event-trigger condition and demonstrate closed-loop stability. Detailed derivations, rigorous stability proofs, computer simulation examples, and downloadable MATLAB® codes are included for each case.

The book begins by providing background on linear and nonlinear systems, NCS, networked imperfections, distributed systems, adaptive dynamic programming and optimal control, stability theory, and optimal adaptive event-triggered controller design in continuous-time and discrete-time for linear, nonlinear and distributed systems. It lays the foundation for reinforcement learning-based optimal adaptive controller use for infinite horizons. The text then:

Introduces event triggered control of linear and nonlinear systems, describing the design of adaptive controllers for them Presents neural network-based optimal adaptive control and game theoretic formulation of linear and nonlinear systems enclosed by a communication network Addresses the stochastic optimal control of linear and nonlinear NCS by using neuro dynamic programming Explores optimal adaptive design for nonlinear two-player zero-sum games under communication constraints to solve optimal policy and event trigger condition Treats an event-sampled distributed linear and nonlinear systems to minimize transmission of state and control signals within the feedback loop via the communication network Covers several examples along the way and provides applications of event triggered control of robot manipulators, UAV and distributed joint optimal network scheduling and control design for wireless NCS/CPS in order to realize industry 4.0 vision

An ideal textbook for senior undergraduate students, graduate students, university researchers, and practicing engineers, Optimal Event Triggered Control Design using Adaptive Dynamic Programming instills a solid understanding of neural network-based optimal controllers under event-sampling and how to build them so as to attain CPS or Industry 4.0 vision.

Optimal Event-triggered Control using Adaptive Dynamic Programming discusses event triggered controller design which includes optimal control and event sampling design for linear and nonlinear dynamic systems including networked control systems (NCS) when the system dynamics are both known and uncertain. The NCS are a first step to realize cyber-physical systems (CPS) or industry 4.0 vision. The authors apply several powerful modern control techniques to the design of event-triggered controllers and derive event-trigger condition and demonstrate closed-loop stability. Detailed derivations, rigorous stability proofs, computer simulation examples, and downloadable MATLAB® codes are included for each case.

The book begins by providing background on linear and nonlinear systems, NCS, networked imperfections, distributed systems, adaptive dynamic programming and optimal control, stability theory, and optimal adaptive event-triggered controller design in continuous-time and discrete-time for linear, nonlinear and distributed systems. It lays the foundation for reinforcement learning-based optimal adaptive controller use for infinite horizons. The text then:

Introduces event triggered control of linear and nonlinear systems, describing the design of adaptive controllers for them Presents neural network-based optimal adaptive control and game theoretic formulation of linear and nonlinear systems enclosed by a communication network Addresses the stochastic optimal control of linear and nonlinear NCS by using neuro dynamic programming Explores optimal adaptive design for nonlinear two-player zero-sum games under communication constraints to solve optimal policy and event trigger condition Treats an event-sampled distributed linear and nonlinear systems to minimize transmission of state and control signals within the feedback loop via the communication network Covers several examples along the way and provides applications of event triggered control of robot manipulators, UAV and distributed joint optimal network scheduling and control design for wireless NCS/CPS in order to realize industry 4.0 vision

An ideal textbook for senior undergraduate students, graduate students, university researchers, and practicing engineers, Optimal Event Triggered Control Design using Adaptive Dynamic Programming instills a solid understanding of neural network-based optimal controllers under event-sampling and how to build them so as to attain CPS or Industry 4.0 vision.

This volume constitutes reviewed and selected papers from the 11th International Advanced Computing Conference, IACC 2021, held in December 2021.The 47 full papers and 4 short papers presented in the volume were thorougly reviewed and selected from 246 submissions. The papers are organized in the following topical sections: application of artificial intelligence and machine learning in healthcare; application of AI for emotion and behaviour prediction; problem solving using reinforcement learning and analysis of data; advance uses of RNN and regression techniques; special intervention of AI.