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Model Predictive Control = Approaches Based on the Extended State Space Model and Extended Non-minimal State Space Model /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Model Predictive Control/ by Ridong Zhang, Anke Xue, Furong Gao.
Reminder of title:	Approaches Based on the Extended State Space Model and Extended Non-minimal State Space Model /
Author:	Zhang, Ridong.
other author:	Xue, Anke.
Description:	XV, 137 p. 28 illus., 25 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Control engineering. -
Online resource:	https://doi.org/10.1007/978-981-13-0083-7
ISBN:	9789811300837

Model Predictive Control = Approaches Based on the Extended State Space Model and Extended Non-minimal State Space Model /
Zhang, Ridong.

Model Predictive ControlApproaches Based on the Extended State Space Model and Extended Non-minimal State Space Model /[electronic resource] :by Ridong Zhang, Anke Xue, Furong Gao. - 1st ed. 2019. - XV, 137 p. 28 illus., 25 illus. in color.online resource.

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.

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. .

ISBN: 9789811300837

Standard No.: 10.1007/978-981-13-0083-7doiSubjects--Topical Terms:

1249728
Control engineering.

LC Class. No.: TJ212-225

Dewey Class. No.: 629.8

Model Predictive Control = Approaches Based on the Extended State Space Model and Extended Non-minimal State Space Model /
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505 0 $a 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.
520 $a 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. .
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