Adaptive control of black-box nonlinear systems using recurrent neural networks

An adaptive control method of black-box nonlinear systems is presented. The control law is derived based on minimizing a suitably chosen performance index, and its implementation requires only the calculation of two key quantities, i.e., the sensitivity between the controlled system input and output and the quasi-one-step-ahead predictive output of the controlled system. In the paper, the sensitivity of the plant is estimated using the recursive rectangular window least square algorithm, and the predictive output is obtained by a recurrent neural network. The simulation results show that the proposed adaptive control method can effectively control a class of unknown nonlinear systems.