Название: Optimal Event-Triggered Control Using Adaptive Dynamic Programming
Автор: Sarangapani Jagannathan, Vignesh Narayanan, Avimanyu Sahoo
Издательство: CRC Press
Год: 2024
Страниц: 346
Язык: английский
Формат: pdf (true)
Размер: 22.6 MB

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.

Recently, neural network-based optimal adaptive controllers that function forward-in-time manner, to perform regulation and trajectory tracking, have been developed using Reinforcement Learning and adaptive dynamic programming both in continuous and discrete time. Controllers designed in discrete time have the important advantage that they can be directly implemented in digital form on modern-day embedded hardware. Unfortunately, the discrete-time design is far more complex than the continuous-time design when Lyapunov stability analysis is used since the first difference in the Lyapunov function is quadratic in the states not linear as in the case of continuous-time. This coupled with uncertainty in system dynamics along with event-triggered sampling and control for uncertain linear and nonlinear systems require an advanced optimal adaptive controller design.

This book presents the neural networks and Q-learning-based event-triggered control design to address regulation and tracking for linear and nonlinear dynamical systems. Several powerful modern control techniques are used in the book for the design of such controllers. Thorough development, rigorous analysis, and simulation examples are presented in each case. Proof sketch are provided for stability.

In this section, we will provide a concise overview of artificial neural networks (NN), emphasizing aspects most pertinent to their applications in closed-loop control of dynamical systems. Numerous other neural network architectures, such as Long Short-Term Memory (LSTMs) networks, autoencoders, Generative Adversarial Networks (GANs), transformers, graph neural networks, reservoir networks, graph convolutional networks, and more advanced generative networks like diffusion models, have been introduced. The history of neural networks is rich and marked by periods of intense innovation, skepticism, and resurgence. From the foundational ideas of McCulloch and Pitts to the present era of Deep Learning and diffusion models, neural networks have evolved into a powerful and versatile tool, finding applications in diverse domains. Artificial neural networks (ANNs) draw inspiration from biological information processing systems, specifically the nervous system and its fundamental unit, the neuron.

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.




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