Model Predictive Control
Approaches Based on the Extended State Space Model and Extended Non-minimal State Space Model
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.
Introduction.- Model Predictive Control Based on Extended State Space Model.- Predictive Functional Control Based on Extended State Space Model.- Model Predictive Control Based on Extended Non-Minimal State Space Model.- Predictive Functional Control Based on Extended Non-minimal State Space Model.- Model Predictive Control Under Constraints.- PID Control Using Extended Non-minimal State Space Model Optimization.- Closed-loop System Performance Analysis.- Model Predictive Control Performance Optimized by Genetic Algorithm.- Industrial Application.- Further Ideas on MPC and PFC Using Relaxed Constrained Optimization.