An adaptive neural network controller for nonlinear MIMO discrete-time systems

In this paper, a multivariable adaptive control approach is proposed for a class of unknown nonlinear multi variable discrete-time systems. An equivalent model in affine-like form is first derived for the original nonaffine discrete-time systems. Then, feedback linearization adaptive control is implemented based on the affine-like equivalent model identified with neural networks. A new NNs weight updating method is proposed based on idea of proportional, integral, differential (PID) controller, which can improve the performance of the system greatly. Simulation result on the electrode regulator system is presented to show the effectiveness of the proposed method.