Adaptive Neural Control of Time-Varying Constrained Direct-Current Motor System with Input Saturation

This paper focuses on the problem of the time-varying constrained direct-current (DC) motor system with input saturation. The radial basis function neural network with less parameters approach is introduced as an identifier to estimate the unknown dynamics in DC motor system. To avoid repeated verification of the feasibility conditions on the virtual control, the nonlinear mapping is employed in each step of backstepping procedure, the prescribed transient performance on tracking error as well as the constraint on system states are directly achieved even when the actuator saturation is taken into account. Based on the Lyapunov analysis, the developed control strategy can ensure that all the closed-loop signals are bounded, the constraints on full system states and tracking error are achieved. The simulation example is used to illustrate the effectiveness of the developed control strategy.