Adaptive Neuro-fuzzy Control System by RBF and GRNN Neural Networks

Recently, adaptive control systems utilizing artificial intelligent techniques are being actively investigated in many applications. Neural networks with their powerful learning capability are being sought as the basis for many adaptive control systems where on-line adaptation can be implemented. Fuzzy logic on the other hand have been proven to be rather popular in many control system applications providing a rule-base like structure. In this paper, an adaptive neuro-fuzzy control system is proposed where the Radial Basis Function neural network (RBF) is implemented as a neuro-fuzzy controller (NFC) and the General Regression neural network (GRNN) as a predictor. The adaptation of the system involves three procedures as follows: (1) tuning of the control actions or rules, (2) trimming of the control actions, and (3) adjustment of the controller output gain. The tuning method is a non-gradient descent method based on the predicted system response, which is able to self-organize the control actions from an initial stage. The trimming scheme can help to reduce the aggressiveness of the particular control rules in order to stabilize the response to the set-points more effectively, while the controller gain adjustment scheme can be applied if the appropriate controller output gain is difficult to be determined heuristically. To show the effectiveness of this proposed methodology, it's performance is compared with the well known Generalized Predictive Control (GPC) technique which has a combination of both adaptive and predictive control schemes. Comparisons are made with respect to transient response, disturbance rejection and changes in plant dynamics, The proposed control system is also applied to control a single link manipulator. The results show that it exhibits robustness and good adaptation capability which can be practically implemented.