1994 Volume 7 Issue 8 Pages 281-286
In this paper, a nonlinear control system incorporating a multi-layered neural network (MNN) with the Self-Tuning Regulator (STR) is designed. The MNN is used to compensate nonlinearity of plant dynamics that is not taken into consideration in the STR design. The control performance is thus improved as compared to the STR alone. Also, the learning time required for convergence and the network size of the MNN can be reduced as compared to conventional MNN based control systems. Because the STR quickly learns the linear part of plant dynamics based upon the least square method. Furthermore, the MNN can be trained on-line with the specialized learning architecture. Because the sensitivity of the system output to the changes of the input is calculated by using plant parameters estimated by the STR. Computer simulations are carried out to show the effectiveness of the proposed method.