Adaptive dynamical tracking control under uncertainty of shunt DC motors

Abstract A new adaptive velocity trajectory tracking control scheme for nonlinear shunt DC electric motors subjected to unmeasurable variable load torque and parametric uncertainty is proposed. Artificial neural networks and dynamical tracking error compensators are synergically combined to avoid dependence on detailed mathematical models of uncertain nonlinear systems and significantly improve the controller robustness and efficiency. The introduced robust tracking control approach can efficiently adapt to diverse uncertain operating scenarios. A priori knowledge or real-time estimation of disturbances and system parameters are unnecessary. The proposed output feedback dynamical tracking control can be extended to a wide class of controllable electric power systems operating under uncertainty. Analytical and numerical results prove the robust and efficient performance of the dynamical tracking control.