Maciej Ławryńczuk 
Nonlinear Predictive Control Using Wiener Models [PDF ebook] 
Computationally Efficient Approaches for Polynomial and Neural Structures

Support


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.

€149.79
payment methods

Table of Content

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

Buy this ebook and get 1 more FREE!
Language English ● Format PDF ● Pages 343 ● ISBN 9783030838157 ● File size 11.9 MB ● Publisher Springer International Publishing ● City Cham ● Country CH ● Published 2021 ● Downloadable 24 months ● Currency EUR ● ID 7933178 ● Copy protection Social DRM

More ebooks from the same author(s) / Editor

18,812 Ebooks in this category