Neuromorphic Fuzzy Controller Design

This chapter shows that a single-input single-output (SISO) Fuzzy Logic Controller (FLC) [Mamdani, 1974; Lee, 1990a, 1990b] can be modelled as a neural network which can be trained using a Genetic Algorithm (GA). The GA is employed to determine the membership functions for the input variable, the quantisation levels of the output variable and the elements of the relation matrix of the FLC. The reasons for such a neuromorphic FLC are provided. The structure of the NN model proposed for an FLC and its GA-based training procedure are explained. Results for the simulated control of a time-delayed linear second-order plant and a non-linear plant are also given. In this chapter, it is assumed that the reader is familiar with fuzzy logic control and genetic algorithms. F or a basic introduction to these topics, see Appendix B and Appendix C.