model predictive control complex von song yan (13 Ergebnisse)

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Zustand: New. Yan Song received the B.Eng. degree in materials science and engineering from Jilin University, Changchun, China, in 2001, the M.Sc. degree in applied mathematics from the University of Electronic Science and Technology of China, Chengdu, China, i.

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Hardcover. Zustand: new. Hardcover. 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 complexitiesThis 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Zustand: new. Hardcover. 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 complexitiesThis 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

HRD. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Zustand: new. Hardcover. 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 complexitiesThis 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.