Adaptive Internal Model Neural Networks Control for Nonlinear System

In this paper is proposed an Adaptive method for Internal Model based on neural network Control (AIMC) for nonlinear time-varying systems. The corresponding algorithm uses a variable learning rate. The proposed method is dependent on the availability of the inverse model of the system and the availability of the internal model in each moment. The bloc of online inverse model and on-line internal model will be used as a bloc of controller. This bloc of controller tries to minimize the error between the system output and the internal model output. The adjustment of the controller bloc runs in each moment. The robustness of the proposed adaptive internal model neural network control strategy is investigated in threes cases; firstly when the system has time-invariant parameters, secondly when it has a time-varying parameters and finally when it’s a noisy timevarying system. The proposed strategy is compared with the Adaptive Direct Inverse Control (ADIC). From the experiments, it is showing that the performance of the AIMC method is much better than the ADIC method. Two different reference command signals are used to test the control system performance, and it is noted that an excellent tracking response is exhibited in the presence of disturbance. Keywords—Nonlinear system; neural network; adaptive control; internal model