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Nonlinear predictive control using wiener models = computationally efficient approaches for polynomial and neural structures /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonlinear predictive control using wiener models/ by Maciej Lawrynczuk.
Reminder of title:	computationally efficient approaches for polynomial and neural structures /
Author:	Lawrynczuk, Maciej.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xxiii, 343 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction to Model Predictive Control -- MPC Algorithms Using Input-Output Wiener Models -- MPC Algorithms Using State-Space Wiener Models -- Conclusions -- Index.
Contained By:	Springer Nature eBook
Subject:	Predictive control. -
Online resource:	https://doi.org/10.1007/978-3-030-83815-7
ISBN:	9783030838157

Nonlinear predictive control using wiener models = computationally efficient approaches for polynomial and neural structures /
Lawrynczuk, Maciej.

Nonlinear predictive control using wiener modelscomputationally efficient approaches for polynomial and neural structures /[electronic resource] :by Maciej Lawrynczuk. - Cham :Springer International Publishing :2022. - xxiii, 343 p. :ill., digital ;24 cm. - Studies in systems, decision and control,v. 3892198-4190 ;. - Studies in systems, decision and control ;v. 389..

Introduction to Model Predictive Control -- MPC Algorithms Using Input-Output Wiener Models -- MPC Algorithms Using State-Space Wiener Models -- Conclusions -- Index.

This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of process nonlinearity. Unfortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant. A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages of neural Wiener models are demonstrated.

ISBN: 9783030838157

Standard No.: 10.1007/978-3-030-83815-7doiSubjects--Topical Terms:

667592
Predictive control.

LC Class. No.: TJ217.6 / .L38 2022

Dewey Class. No.: 629.8

Nonlinear predictive control using wiener models = computationally efficient approaches for polynomial and neural structures /
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490 1 $a Studies in systems, decision and control, $x 2198-4190 ; $v v. 389
505 0 $a Introduction to Model Predictive Control -- MPC Algorithms Using Input-Output Wiener Models -- MPC Algorithms Using State-Space Wiener Models -- Conclusions -- Index.
520 $a This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of process nonlinearity. Unfortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant. A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages of neural Wiener models are demonstrated.
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