An adaptive fuzzy logic controller based on real coded quantum-inspired evolutionary algorithm

In this paper, fuzzy logic control systems and real coded quantum inspired evolutionary algorithm (RCQIEA) are integrated for intelligent control. Here, RCQIEAs is utilized as an adaptive method for selection and definition of fuzzy control rules and for tuning the parameters of membership function for each fuzzy control rule in two different ways. The majority of fuzzy logic controllers (FLCs) to date are working based on the expert knowledge base derived from heuristic knowledge of experienced operators. These approaches are difficult and time consuming for experts. Moreover, because manual coded FLCs use expert knowledge, there is no guarantee that the FLCs obtained will have sufficiently good performance, especially for a complex system problem with a large number of input variables. On the contrary, our proposed approach is an automatic knowledge acquisition learning method for generating or adapting FLCs using RCQIEA. In order to check the effectiveness of our proposed approach, it has been applied to solve the truck-and-trailer controller, which is well known test-bed for fuzzy control systems. The fuzzy controller obtained by the proposed approach performs better and effectively realizes the trajectory control of the truck with trailer.