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Model predictive control = approache...
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Zhang, Ridong.
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Model predictive control = approaches based on the extended state space model and extended non-minimal state space model /
Record Type:
Electronic resources : 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.
[NT 15003449]:
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.
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 control
approaches 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:
667592
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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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.
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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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Intelligent Technologies and Robotics (Springer-42732)
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EB TJ217.6 .Z436 2019
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