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Nonlinear Predictive Control Using Wiener Models = Computationally Efficient Approaches for Polynomial and Neural Structures /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Nonlinear Predictive Control Using Wiener Models/ by Maciej Ławryńczuk.
Reminder of title:	Computationally Efficient Approaches for Polynomial and Neural Structures /
Author:	Ławryńczuk, Maciej.
Description:	XXIII, 343 p. 167 illus., 121 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Control engineering. -
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 /
Ławryńczuk, Maciej.

Nonlinear Predictive Control Using Wiener ModelsComputationally Efficient Approaches for Polynomial and Neural Structures /[electronic resource] :by Maciej Ławryńczuk. - 1st ed. 2022. - XXIII, 343 p. 167 illus., 121 illus. in color.online resource. - Studies in Systems, Decision and Control,3892198-4190 ;. - Studies in Systems, Decision and Control,27.

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:

1249728
Control engineering.

LC Class. No.: TJ212-225

Dewey Class. No.: 629.8

Nonlinear Predictive Control Using Wiener Models = Computationally Efficient Approaches for Polynomial and Neural Structures /
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