On-line fuzzy identification for advanced intelligent controller

Abstract

The paper presents the identification issues of the self-tuning nonlinear controller ASPECT* (advanced control algorithms for programmable logic controllers). The controller is implemented on a simple PLC platform with an extra mathematical coprocessor but is intended for the advanced control of complex processes. The model of the controlled plant is obtained by means of experimental modelling using an online learning procedure that combines model identification with pre-and post-identification steps that provide reliable operation. It is shown that acceptable performance of the system is obtained despite the difficult conditions it may encounter, such as nonlinearity of the plant, slowly varying parameters of the plant, high level noise etc.