MIMO fuzzy internal model control

Model-based controllers are now beginning to gain widespread acceptance in industry. However, the majority of these controllers are based on linear mo dels and performance in controlling the non-linear processes common in the chemical industry is sub-optimal. The use of a non-linear model could yield significant improvements in control performance. In this study a relationa l model from a fuzzy input space to a crisp output space is constructed by applying a least-squares identification technique to past process data. Thi s model is termed a crisp-consequent fuzzy relational model (ccFRM) and is capable of giving an accurate representation of a non-linear system. A nove l inversion method is presented which allows the ccFRM to be inverted and u sed within the well-known IMC structure. This new controller is termed a fu zzy internal model controller (FIMC) and test results are presented showing the FIMC performing both servo and regulatory action on a multi-variable s imulated pH system. This process is extremely non-linear and exhibits sever e interaction effects and is consequently a very difficult system to contro l. The simulation is introduced in detail, as are the tests carried out, an d the performance of the FIMC in these tests is found to be encouraging. (C ) 2000 Elsevier Science Ltd. All rights reserved.