Time-varying output constraint for DC motor control

This paper presents a control problem for a class of DC motor nonlinear systems with output constraints, and the constraints are in time-varying form. Based on the dynamics system of the DC motor, a nonlinear model of the system with modeling uncertainties is established. Then, an asymmetric time-varying Barrier Lyapunov Function (BLF) is proposed to guarantee the output to meet the constraints and the radial basis function neural networks (RBFNNs) are employed to approximate unknown function. By employing the backstepping method, the adaptive law based on DC motor nonlinear model is given. Then, all the signals in the closed-loop system are shown to be semi globally uniformly ultimately bounded, the output constraint is satisfied, as well as the tracking error converges to a bounded compact set.