Neural network-based optimal adaptive tracking using genetic algorithms

This paper presents the use of neural networks (NNs) and genetic algorithms (GAs) to enhance the output tracking performance of partly known robotic systems. Two of the most potential approaches of adaptive control, i.e., the concept of variable structure control (VSC) and NN-based adaptive control, are ingeniously combined using GAs to achieve high-performance output tracking. GA is used to make the maximum use of different performance characteristics of two self-adaptive NN modules by finding the switching function which best combines them. The method will be valid for any rigid revolute robot system. Computer simulations on our active binocular head are included for illustration and verification.