Direct Model Reference Adaptive Controller Based-On Neural-Fuzzy Techniques for Nonlinear Dynamical Systems

This paper presents a direct neural-fuzzy-based Model Reference Adaptive Controller (MRAC) for nonlinear dynamical systems with unknown parameters. The two-phase learning is implemented to perform structure identification and parameter estimation for the controller. In the first phase, similarity index-based fuzzy c-means clustering technique extracts the fuzzy rules in the premise part for the neural-fuzzy controller. This technique enables the recruitment of rule parameters in accordance to the number of clusters and kernel centers it automatically generated. In the second phase, the parameters of the controller are directly tuned from the training data via the tracking error. The consequent parts of the rules are thus determined. This iterative process employs Radial Basis Function Neural Network (RBFNN) structure with a reference model to provide a closed-loop performance feedback.