Learning fuzzy rules with Tabu Search: an application to control

In this paper, we present a new approach for the automatic definition of the fuzzy rules for a fuzzy controller based on the use of the tabu search (TS) scheme. We show also how the application of the TS process to the learning of a fuzzy rule base can be improved using heuristic symbolic meta rules. The paper is divided in two parts. The first part (Sections I-III) presents an introduction to TS and different learning schemes which can be used to apply it for the determination of the fuzzy control rules. The second part (Sections IV-VI) illustrates the application of the proposed techniques to a specific control problem - the parking of a truck and trailer. In particular, Section V illustrates the definition of a rule base for a static fuzzy controller, while Section VI presents the construction of an adaptive parking controller.