Neural fuzzy control of unstable nonlinear systems

A Fuzzy Adaptive Learning COntrol Network (FALCON) is proposed for the realization of a fuzzy logic control system. An on-line structure/parameter learning algorithm, called FALCON-ART, can on-line partition the input/output spaces, tune membership functions and find proper fuzzy logic rules dynamically without any a priori knowledge or even any initial information on these. The FALCON-ART requires exact supervised training data for learning. In some real-time applications, exact training data may be expensive or even impossible to obtain. To solve this problem, a Reinforcement Fuzzy Adaptive Learning COntrol Network (RFALCON) is further proposed. By combining a proposed on-line supervised structure/parameter learning technique, the temporal difference method, and the stochastic exploratory algorithm, a on-line supervised structure/parameter learning algorithm, called RFALCON-ART, is proposed for constructing the RFALCON dynamically.