Adaptive control of servo mechanism with a two-layered neural network

A two-layered neural network was applied to adaptive control of a servo mechanism. The two-layered neural network is simple and can be built in a structure corresponding to inverse dynamics of a controlled plant. A demand signal, previous output signals of a plant, and previous control input signals are fed into the network. The output of the network is control input and is fed into a controlled plant. Initial weights can be set using information on nominal plant parameters. The weights are updated by a back propagation strategy with a normalized learning rate which may specify the rate of convergence of learning. Through experiments using an electrohydraulic servo motor system, the validity of the neural network controller was examined. Adaptation function of the neural network was demonstrated.