Model Predictive Control for Complex Dynamic Systems : Analysis and Synthesis

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Model Predictive Control for Complex Dynamic Systems : Analysis and Synthesis

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  • 製本 Hardcover:ハードカバー版/ページ数 272 p.
  • 言語 ENG
  • 商品コード 9781041174400

Full Description

Model Predictive Control (MPC) has advanced as a robust method for managing complex dynamic systems, surpassing traditional control strategies in performance and constraint handling. This book explores MPC theory and applications, focusing on robust MPC (RMPC) for systems with uncertainties. It examines three system types: networked systems, stochastic switching systems, and nonlinear hybrid systems. It addresses challenges in networked interventions, Markovian jump systems, and nonlinear systems under communication constraints.

Integrates model predictive control, network-induced constraints, cyber-security issues, and advanced communication protocols
Covers control and state estimation with a focus on dynamic network systems with complex sampling.
Considers and models network-induced complexities
Employs several analysis techniques to overcome the recent mathematical/computational difficulties for discrete-time systems
Deals with practical engineering problems for complex dynamic systems with different kinds of scenario-induced complexities or framework-induced complexities

This book is aimed at graduate students and researchers in networks, signal processing, controls, dynamic complex systems.

Contents

1 Introduction 2 H2/H∞ MPC for Polytopic Uncertain under Weighted MEF-TOD Protocol 3 Security-based MPC for Polytopic Uncertain Systems Subject to Deception Attacks and Round-Robin Protocol 4 N-Step MPC for Uncertain Systems with Persistent Bounded Disturbances under Stochastic Communication Protocol 5 Observer-Based N-step MPC for Networked Control Systems under Encoding-Decoding Communication Protocol 6 Resilient Robust MPC for Markovian Jump Switching Systems under Asynchronous Detected Scenario 7 Asynchronous Constrained MPC for Markovian Jump Switching Systems: An Optimizing Prediction Dynamics Approach 8 Efficient MPC for Nonlinear Systems in Interval Type-2 T-S Fuzzy Form under Round-Robin Protocol 9 Efficient MPC for Nonlinear Systems in Interval Type-2 T-S Fuzzy Form under Stochastic Communication Protocol 10 Resilient MPC for Cyber-Physical Systems with State Saturation under TOD Protocol: An ADT approach 11 Conclusions and Future Topics 12 Bibliography

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