9789819697779 - Robust Iterative Learning Control of Industrial Batch Systems: 22 di Liu, Tao; Hao, Shoulin; Wang, Youqing; Li, Dewei (10 risultati)

Hardcover. Condizione: new. Hardcover. This book offers advanced iterative learning control (ILC) and optimization methods for industrial batch systems, facilitating engineering applications subject to time- and batch-varying process uncertainties that could not be effectively addressed by the existing ILC methods. In particular, advanced ILC designs based on the classical proportional-integral-derivative (PID) control loop are presented for the convenience of application, which could not only realize perfect tracking of the desired output trajectory under repetitive process uncertainties and disturbance, but also maintain robust tracking against time-varying uncertainties and disturbance. Moreover, optimization-based ILC designs are provided to deal with the input and/or output constraints of batch process operation, based on the mode predictive control (MPC) principle for process optimization. Furthermore, predictor-based ILC designs are given to deal with time delay in the process input, state or output as often encountered in practice, which could obtain evidently improved control performance compared to the developed ILC methods mainly devoted to delay-free batch processes. In addition, data-driven ILC methods are also presented for application to batch operation systems with unknown dynamics and time-varying uncertainties. Benchmark examples from the existing literature are used to demonstrate the advantages of the proposed ILC methods, along with real applications to industrial injection molding machines, 6-degree-of-freedom robotic manipulator, and refrigerated/heating circulators of pharmaceutical crystallizers. This book will be a valuable source of information for control engineers and researchers in industrial process control theory and engineering field. It can also be used as an advanced textbook for undergraduate and graduate students in control engineering, process system engineering, chemical engineering, mechanical engineering, electrical engineering, biomedical engineering and industrial automation engineering. This book offers advanced iterative learning control (ILC) and optimization methods for industrial batch systems, facilitating engineering applications subject to time- and batch-varying process uncertainties that could not be effectively addressed by the existing ILC methods. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condizione: new. Hardcover. This book offers advanced iterative learning control (ILC) and optimization methods for industrial batch systems, facilitating engineering applications subject to time- and batch-varying process uncertainties that could not be effectively addressed by the existing ILC methods. In particular, advanced ILC designs based on the classical proportional-integral-derivative (PID) control loop are presented for the convenience of application, which could not only realize perfect tracking of the desired output trajectory under repetitive process uncertainties and disturbance, but also maintain robust tracking against time-varying uncertainties and disturbance. Moreover, optimization-based ILC designs are provided to deal with the input and/or output constraints of batch process operation, based on the mode predictive control (MPC) principle for process optimization. Furthermore, predictor-based ILC designs are given to deal with time delay in the process input, state or output as often encountered in practice, which could obtain evidently improved control performance compared to the developed ILC methods mainly devoted to delay-free batch processes. In addition, data-driven ILC methods are also presented for application to batch operation systems with unknown dynamics and time-varying uncertainties. Benchmark examples from the existing literature are used to demonstrate the advantages of the proposed ILC methods, along with real applications to industrial injection molding machines, 6-degree-of-freedom robotic manipulator, and refrigerated/heating circulators of pharmaceutical crystallizers. This book will be a valuable source of information for control engineers and researchers in industrial process control theory and engineering field. It can also be used as an advanced textbook for undergraduate and graduate students in control engineering, process system engineering, chemical engineering, mechanical engineering, electrical engineering, biomedical engineering and industrial automation engineering. This book offers advanced iterative learning control (ILC) and optimization methods for industrial batch systems, facilitating engineering applications subject to time- and batch-varying process uncertainties that could not be effectively addressed by the existing ILC methods. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condizione: New.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers advanced iterative learning control (ILC) and optimization methods for industrial batch systems, facilitating engineering applications subject to time- and batch-varying process uncertainties that could not be effectively addressed by the existing ILC methods. In particular, advanced ILC designs based on the classical proportional-integral-derivative (PID) control loop are presented for the convenience of application, which could not only realize perfect tracking of the desired output trajectory under repetitive process uncertainties and disturbance, but also maintain robust tracking against time-varying uncertainties and disturbance. Moreover, optimization-based ILC designs are provided to deal with the input and/or output constraints of batch process operation, based on the mode predictive control (MPC) principle for process optimization. Furthermore, predictor-based ILC designs are given to deal with time delay in the process input, state or output as often encountered in practice, which could obtain evidently improved control performance compared to the developed ILC methods mainly devoted to delay-free batch processes. In addition, data-driven ILC methods are also presented for application to batch operation systems with unknown dynamics and time-varying uncertainties. Benchmark examples from the existing literature are used to demonstrate the advantages of the proposed ILC methods, along with real applications to industrial injection molding machines, 6-degree-of-freedom robotic manipulator, and refrigerated/heating circulators of pharmaceutical crystallizers. This book will be a valuable source of information for control engineers and researchers in industrial process control theory and engineering field. It can also be used as an advanced textbook for undergraduate and graduate students in control engineering, process system engineering, chemical engineering, mechanical engineering, electrical engineering, biomedical engineering and industrial automation engineering.

Hardcover. Condizione: new. Hardcover. This book offers advanced iterative learning control (ILC) and optimization methods for industrial batch systems, facilitating engineering applications subject to time- and batch-varying process uncertainties that could not be effectively addressed by the existing ILC methods. In particular, advanced ILC designs based on the classical proportional-integral-derivative (PID) control loop are presented for the convenience of application, which could not only realize perfect tracking of the desired output trajectory under repetitive process uncertainties and disturbance, but also maintain robust tracking against time-varying uncertainties and disturbance. Moreover, optimization-based ILC designs are provided to deal with the input and/or output constraints of batch process operation, based on the mode predictive control (MPC) principle for process optimization. Furthermore, predictor-based ILC designs are given to deal with time delay in the process input, state or output as often encountered in practice, which could obtain evidently improved control performance compared to the developed ILC methods mainly devoted to delay-free batch processes. In addition, data-driven ILC methods are also presented for application to batch operation systems with unknown dynamics and time-varying uncertainties. Benchmark examples from the existing literature are used to demonstrate the advantages of the proposed ILC methods, along with real applications to industrial injection molding machines, 6-degree-of-freedom robotic manipulator, and refrigerated/heating circulators of pharmaceutical crystallizers. This book will be a valuable source of information for control engineers and researchers in industrial process control theory and engineering field. It can also be used as an advanced textbook for undergraduate and graduate students in control engineering, process system engineering, chemical engineering, mechanical engineering, electrical engineering, biomedical engineering and industrial automation engineering. This book offers advanced iterative learning control (ILC) and optimization methods for industrial batch systems, facilitating engineering applications subject to time- and batch-varying process uncertainties that could not be effectively addressed by the existing ILC methods. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers advanced iterative learning control (ILC) and optimization methods for industrial batch systems, facilitating engineering applications subject to time- and batch-varying process uncertainties that could not be effectively addressed by the existing ILC methods. In particular, advanced ILC designs based on the classical proportional-integral-derivative (PID) control loop are presented for the convenience of application, which could not only realize perfect tracking of the desired output trajectory under repetitive process uncertainties and disturbance, but also maintain robust tracking against time-varying uncertainties and disturbance. Moreover, optimization-based ILC designs are provided to deal with the input and/or output constraints of batch process operation, based on the mode predictive control (MPC) principle for process optimization. Furthermore, predictor-based ILC designs are given to deal with time delay in the process input, state or output as often encountered in practice, which could obtain evidently improved control performance compared to the developed ILC methods mainly devoted to delay-free batch processes. In addition, data-driven ILC methods are also presented for application to batch operation systems with unknown dynamics and time-varying uncertainties. Benchmark examples from the existing literature are used to demonstrate the advantages of the proposed ILC methods, along with real applications to industrial injection molding machines, 6-degree-of-freedom robotic manipulator, and refrigerated/heating circulators of pharmaceutical crystallizers. This book will be a valuable source of information for control engineers and researchers in industrial process control theory and engineering field. It can also be used as an advanced textbook for undergraduate and graduate students in control engineering, process system engineering, chemical engineering, mechanical engineering, electrical engineering, biomedical engineering and industrial automation engineering. 283 pp. Englisch.

Buch. Condizione: Neu. Robust Iterative Learning Control of Industrial Batch Systems | Tao Liu (u. a.) | Buch | Intelligent Control and Learning Systems | xiv | Englisch | 2025 | Springer | EAN 9789819697779 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book offers advanced iterative learning control (ILC) and optimization methods for industrial batch systems, facilitating engineering applications subject to time- and batch-varying process uncertainties that could not be effectively addressed by the existing ILC methods. In particular, advanced ILC designs based on the classical proportional-integral-derivative (PID) control loop are presented for the convenience of application, which could not only realize perfect tracking of the desired output trajectory under repetitive process uncertainties and disturbance, but also maintain robust tracking against time-varying uncertainties and disturbance. Moreover, optimization-based ILC designs are provided to deal with the input and/or output constraints of batch process operation, based on the mode predictive control (MPC) principle for process optimization. Furthermore, predictor-based ILC designs are given to deal with time delay in the process input, state or output as often encountered in practice, which could obtain evidently improved control performance compared to the developed ILC methods mainly devoted to delay-free batch processes. In addition, data-driven ILC methods are also presented for application to batch operation systems with unknown dynamics and time-varying uncertainties. Benchmark examples from the existing literature are used to demonstrate the advantages of the proposed ILC methods, along with real applications to industrial injection molding machines, 6-degree-of-freedom robotic manipulator, and refrigerated/heating circulators of pharmaceutical crystallizers. This book will be a valuable source of information for control engineers and researchers in industrial process control theory and engineering field. It can also be used as an advanced textbook for undergraduate and graduate students in control engineering, process system engineering, chemical engineering, mechanical engineering, electrical engineering, biomedical engineering and industrial automation engineering.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 300 pp. Englisch.

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.