Neural network based output feedback control for DC motors with asymptotic stability

Measurement noise and unknown dynamics including friction, parametric uncertainties, external disturbances and unmodeled dynamics, broadly exist in any electromechanical system and worsen their control performance. To address this issue, this paper develops an output feedback control scheme with neural network (NN) based unknown dynamics compensation for DC motor systems. First, to avert using the noise-polluted velocity signal, a state observer with an online adapted gain is adopted, which attenuates the impact of measurement noise on tracking accuracy and reduces the conservatism of observer gain selection. Second, one NN with low computation and ease to design, is employed for unknown dynamics compensation, which is beneficial for practical applications. Then a composite control law is constructed to achieve high-accuracy tracking performance. The stability analysis reveals the tracking error can asymptotically converge to zero while facing time-variant unknown dynamics. Results of comparative experiments validate the superiority of the presented method.