Optimal Networked Control Systems with MATLAB

Optimal Networked Control Systems with MATLAB® discusses optimal controller design in discrete time for networked control systems (NCS). The authors apply several powerful modern control techniques in discrete time to the design of intelligent controllers for such NCS. Detailed derivations, rigorous stability proofs, computer simulation examples, and downloadable MATLAB® codes are included for each case. The book begins by providing background on NCS, networked imperfections, dynamical systems, stability theory, and stochastic optimal adaptive controllers in discrete time for linear and nonlinear systems. It lays the foundation for reinforcement learning-based optimal adaptive controller use for finite and infinite horizons. The text then: Introduces quantization effects for linear and nonlinear NCS, describing the design of stochastic adaptive controllers for a class of linear and nonlinear systems Presents two-player zero-sum game-theoretic formulation for linear systems in input–output form enclosed by a communication network Addresses the stochastic optimal control of nonlinear NCS by using neuro dynamic programming Explores stochastic optimal design for nonlinear two-player zero-sum games under communication constraints Treats an event-sampled distributed NCS to minimize transmission of state and control signals within the feedback loop via the communication network Covers distributed joint optimal network scheduling and control design for wireless NCS, as well as the effect of network protocols on the wireless NCS controller design An ideal reference for graduate students, university researchers, and practicing engineers, Optimal Networked Control Systems with MATLAB® instills a solid understanding of neural network controllers and how to build them.

Introduction to Networked Control SystemsOverview of Networked Control TechniquesChallenges in Networked Control SystemsCurrent ResearchReferences Background on Lyapunov Stability and Stochastic Optimal ControlDeterministic Dynamical SystemsMathematical BackgroundProperties of Dynamical SystemsNonlinear Stability Analysis and Controls DesignStochastic Discrete-Time ControlProblemsReferences Optimal Adaptive Control of Uncertain Linear Network Control SystemsTraditional Control Design and Stochastic Riccati Equation-Based SolutionFinite-Horizon Optimal Adaptive ControlExtensions to Infinite HorizonConclusionsProblemsAppendicesReferences Optimal Control of Unknown Quantized Network Control SystemsBackgroundFinite-Horizon Optimal Control of Linear QNCSFinite-Horizon Optimal Control of Nonlinear QNCSConclusionsProblemsAppendicesReferences Optimal Control of Uncertain Linear Networked Control Systems in Input–Output Form with Disturbance InputsTraditional Two-Player Zero-Sum Game Design and Game-Theoretic Riccati Equation-Based SolutionInfinite-Horizon Optimal Adaptive DesignConclusionsProblemsAppendicesReferences Optimal Control of Uncertain Nonlinear Networked Control Systems via Neuro Dynamic ProgrammingTraditional Nonlinear Optimal Control Design and HJB Equation-Based SolutionFinite-Horizon Optimal Control for NNCSExtensions to Infinite HorizonConclusionsProblemsReferences Optimal Design for Nonlinear Two-Player Zero-Sum Games under Communication ConstraintsTraditional Stochastic Optimal Control Design for Two-Player Zero-Sum GameNNCS Two-Player Zero-Sum GameFinite-Horizon Optimal Adaptive DesignSimulation ResultsConclusionsProblemsAppendicesReferences Distributed Joint Optimal Network Scheduling and Controller Design for Wireless Networked Control SystemsBackground of Wireless Networked Control SystemsWireless Networked Control Systems CodesignConclusionsProblemsReferences Event-Sampled Distributed Networked Control SystemsDistributed Networked Control SystemsOptimal Adaptive Event-Sampled ControlSimulationConclusionsProblemsReferences Optimal Control of Uncertain Linear Control Systems under a Unified Communication ProtocolOptimal Control Design under Unified Communication Protocol FrameworkClosed-Loop System StabilitySimulation ResultsConclusionsProblemsAppendicesReferences

Jagannathan Sarangapani (referred to as S. Jagannathan) is a Rutledge-Emerson endowed chair professor of electrical and computer engineering and the site director for the National Science Foundation Industry/University Cooperative Research Center on Intelligent Maintenance Systems at the Missouri University of Science and Technology, Rolla, USA (former University of Missouri-Rolla, USA). Widely published and highly decorated with 20 US patents, he is a fellow of the Institute of Measurement and Control, UK, and the Institution of Engineering and Technology, UK. He has been on the organizing committees of several IEEE conferences, and served as the IEEE Control Systems Society Intelligent Control Technical Committee chair. Hao Xu earned his master’s degree in electrical engineering from Southeast University, Nanjing, China, in 2009, and his Ph.D from the Missouri University of Science and Technology, Rolla, USA (formerly, the University of Missouri-Rolla, USA), in 2012. Currently, he is an assistant professor in the College of Science and Engineering and the director of the Unmanned Systems Research Laboratory at Texas A&M University–Corpus Christi, USA. His research interests include autonomous unmanned aircraft systems, multi-agent systems, wireless passive sensor networks, localization, detection, networked control systems, cyber-physical systems, distributed network protocol design, optimal control, and adaptive control.