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 frame-work-induced complexities

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

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 frame-work-induced complexities

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

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 frame-work-induced complexities

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