Adaptive control of direct current motors

The improved design of model reference adaptive control (MRAC) to control of a DC motor speed is analyzed in this paper. It is well-known that standard MRAC exhibits high-gain control behaviors when a large adaptive gain is used to achieve fast adaptation in order to reduce tracking error rapidly. High gain control creates high-frequency oscillations that can lead to instability. The improved design of MRAC by introducing a nonlinear adaptation gain tuned by neural net subsystem are proposed. The structure of neural net as a four-channel system with nonlinear activation functions of each channel, the shape of which is adjusted in the process of genetic optimization. The influence of varying load disturbance is compensated by changing the adaptation gain parameter. The results of computational experiments in MATLAB-SIMULINK, given in the article, show that the proposed approach to design MRAC allows for a significant improvement of quality of transients when compared with conventional MRAC control scheme.