Intelligent control using a neuro-fuzzy network

Abstract

Intelligent control techniques have emerged to overcome some deficiencies in conventional control methods in dealing with complex real-world systems. These problems include knowledge adaptation, learning, and expert knowledge incorporation. In this paper, a hybrid network that combines fuzzy inferencing and neural networks is used to model and to control complex dynamic systems. The network takes advantage of the learning algorithms developed for neural networks to generate the knowledge base used in fuzzy inferencing. The network as used to model and to control a robot arm with flexible pneumatic actuator. Comparison with a nonlinear control technique used for the robot joints is also presented.