Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures: 389 (Studies in Systems, Decision and Control, 389)

Lawrynczuk, Maciej

ISBN 10: 303083817X / ISBN 13: 9783030838171

Published by Springer, 2022

About this Item

366 pages. 9.25x6.10x0.76 inches. In Stock.

Seller Inventory # x-303083817X

Bibliographic Details

• Title: Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures: 389 (Studies in Systems, Decision and Control, 389)
• Publisher: Springer
• Publication Date: 2022
• Language: English
• ISBN 10: 303083817X
• ISBN 13: 9783030838171
• Binding: Paperback
• Condition: Brand New
• Weight: 0.56 kilograms

About this title

Synopsis

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 ofneural Wiener models are demonstrated.