Neural network control-based adaptive design for a class of DC motor systems with the full state constraints

In the paper, an adaptive neural controller for the tracking problem of a direct-current (DC) motor is investigated. Because the unknown functions are included in the systems, the neural networks are used to estimate the unknown functions. In this study, the state variables of DC motor are required to be constrained in the compact set. The main contribution of this paper is that the proposed scheme is successfully to integrate barrier Lyapunov function to avoid the violation of the constraints. Based on Lyapunov analysis, it is proved that the output of the DC motor follows a desired trajectory and all the signals of the systems are guaranteed to be bounded. A simulation result is shown to confirm the effectiveness of the proposed scheme.