Evaluation of a Multi-Variable Self-Learning Fuzzy Logic Controller

In spite of the usefulness of fuzzy control, its main drawback comes from lack of a systematic control design methodology. The most challenging aspect of the design of a fuzzy logic controller is the elicitation of the control rules for its rule base. In this paper, a scheme capable of elicitation of acceptable rules for multivariable fuzzy logic controllers is derived by extending an algorithm that enables a single-input-single-output fuzzy logic controller to self-learn its rule-base. The performance of the proposed self-learning procedure is investigated and evaluated by means of simulation studies of a hypothetical plant. The results obtained indicate that the approach could be effective in the control of nonlinear multivariable industrial processes

A Self-Organising Fuzzy Logic Controller

One major drawback of fuzzy logic controllers is the difficulty encountered in the construction of a rule- base that is suitable for the controlled process. In this paper we tackle this problem by proposing an algorithm that allows a designer to initially specify a possibly inaccurate rule-base, which is then made more and more accurate in the course of operation of the control system. The effectiveness of the proposed self-organizing procedure has been investigated by means of computer simulation. The results of the simulation studies indicate that the proposed algorithm is effective

Effect of Varying Controller Parameters on the Performance of a Fuzzy Logic Control System

This paper presents the results of computer simulation studies designed to isolate the effects of the major parameters of a fuzzy logic controller namely the range of the universe of discourse, the extent of overlap of the fuzzy sets, the rules in the rule base and the modes of the output fuzzy sets on the performance of a fuzzy logic control system. The controlled process was modeled by a nonlinear differential equation that was solved using the Runge-Kutta numerical method. The results show that varying the range of the universe of discourse of the inputs to the fuzzy controller affects both the transient response and the steady state error of the system, and that a desired system response could be achieved by adjusting the modes of the output fuzzy sets given a fairly good rule base. It has also been shown that the system response could be fine-tuned by varying the overlap of the input fuzzy sets.