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