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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.
Published:	Singapore :Springer Singapore : : 2019.,
Description:	xv, 137 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Predictive control. -
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. - Singapore :Springer Singapore :2019. - xv, 137 p. :ill. (some col.), digital ;24 cm.

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:

557049
Predictive control.

LC Class. No.: TJ217.6 / .Z436 2019

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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245 1 0 $a Model predictive control $h [electronic resource] : $b approaches based on the extended state space model and extended non-minimal state space model / $c by Ridong Zhang, Anke Xue, Furong Gao.
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300 $a xv, 137 p. : $b ill. (some col.), digital ; $c 24 cm.
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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650 2 4 $a Calculus of Variations and Optimal Control; Optimization. $3 593942
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