Recurrent neural tracking control based on multivariable robust adaptive gradient-descent training algorithm

In this paper, a recurrent neural network (RNN) based robust tracking controller is designed for a class of multiple-input-multiple-output discrete time nonlinear systems. The RNN is used in the closed-loop system to estimate online unknown nonlinear system function. A multivariable robust adaptive gradient-descent training algorithm is developed to train RNN. The weight convergence and system stability are proven in the sense of Lyapunov function. Simulation results are presented for a two-link robot tracking control problem.