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.