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Complexity
Volume 2017 (2017), Article ID 9241254, 8 pages
https://doi.org/10.1155/2017/9241254
Research Article

A Gain-Scheduling PI Control Based on Neural Networks

Dipartimento di Ingegneria Meccanica, Chimica e dei Materiali, Università degli Studi di Cagliari, Via Marengo 2, 09123 Cagliari, Italy

Correspondence should be addressed to Stefania Tronci; ti.acinu.mcmid@icnort.ainafets

Received 13 July 2017; Revised 19 September 2017; Accepted 24 September 2017; Published 19 October 2017

Academic Editor: Jing Na

Copyright © 2017 Stefania Tronci and Roberto Baratti. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a gain-scheduling design technique that relies upon neural models to approximate plant behaviour. The controller design is based on generic model control (GMC) formalisms and linearization of the neural model of the process. As a result, a PI controller action is obtained, where the gain depends on the state of the system and is adapted instantaneously on-line. The algorithm is tested on a nonisothermal continuous stirred tank reactor (CSTR), considering both single-input single-output (SISO) and multi-input multi-output (MIMO) control problems. Simulation results show that the proposed controller provides satisfactory performance during set-point changes and disturbance rejection.