Designing an effective Fuzzy Logic Controller based on Quantum Evolutionary Algorithm

This paper proposes a new approach based on Quantum Evolutionary Algorithm (QEA) for effective selection and definition of fuzzy if-then rules to design Fuzzy Logic Controllers (FLCs). The majority of works done on designing FLCs were based on knowledgebase derived from imprecise heuristic knowledge of experienced operators or persons but they were difficult and time consuming to evaluate. The proposed approach decomposes the test problem in such a way that leads to more effective knowledge acquisition and improved control performance in fuzzy control. In this self-learning adaptive method, Truck backer-upper problem, an excellent test-bed for fuzzy control systems is considered as test problem. Each rule base is represented by a real-coded triploid chromosome. At each generation of QEA, rules are updated using Complementary Double Mutation Operator (CDMO) and Discrete Crossover (DC). The experimental results on backing up the truck problem show that the proposed approach to design FLCs do better in terms of time needed to backing up the truck.