Abstract:
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.
