Model Predictive Control
Approaches Based on the Extended State Space Model and Extended Non-minimal State Space Model
Nonfiction, Science & Nature, Science, Other Sciences, System Theory, Technology, Automation
| Author: | Ridong Zhang, Anke Xue, Furong Gao | ISBN: | 9789811300837 |
| Publisher: | Springer Singapore | Publication: | August 14, 2018 |
| Imprint: | Springer | Language: | English |
| Author: | Ridong Zhang, Anke Xue, Furong Gao |
| ISBN: | 9789811300837 |
| Publisher: | Springer Singapore |
| Publication: | August 14, 2018 |
| Imprint: | Springer |
| Language: | English |
This monograph introduces the authors’ work on model predictive control system design using extended state space and extended non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including the basic control algorithms, the extension to predictive functional control, constrained control, closed-loop system analysis, model predictive control optimization-based PID control, genetic algorithm optimization-based model predictive control, and industrial applications. Providing important insights, useful methods and practical algorithms that can be used in chemical process control and optimization, it offers a valuable resource for researchers, scientists and engineers in the field of process system engineering and control engineering.
This monograph introduces the authors’ work on model predictive control system design using extended state space and extended non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including the basic control algorithms, the extension to predictive functional control, constrained control, closed-loop system analysis, model predictive control optimization-based PID control, genetic algorithm optimization-based model predictive control, and industrial applications. Providing important insights, useful methods and practical algorithms that can be used in chemical process control and optimization, it offers a valuable resource for researchers, scientists and engineers in the field of process system engineering and control engineering.
