Online performance evaluation of a self-learning fuzzy logic controller applied to nonlinear processes

The generation of rule-bases in conventional fuzzy logic controllers can be a difficult and time consuming problem for implementation by process operators thus affecting their wider applicability. A self-learning fuzzy logic control (SLFLC) offers a possible solution. A robustness study is therefore presented to evaluate the performance of a proposed SLFLC by analysing its transient performance for a variety of online tests and examining its ability to generate a consistent set of rules, based on a predetermined criteria. The results presented show that even with a limited knowledge of the process, the self-learning procedure is able to develop a suitable set of rules and produce a satisfactory process performance with some degree of robustness and repeatability when applied to a nonlinear single-input single-output (SISO) or multi-input multi-output (MIMO) laboratory liquid-level processes