Neural network adaptive control for a DC motor with bounded constraints

Aimed at the problems that the output and state constraints of the motor were limited within a bounded range, and there are unknown dynamics (nonlinear time-varying factors including friction, parameter uncertainty, external interference and others) that affect the control performance of the system, in the report, a neural network adaptive control algorithm based on integral barrier Lyapunov function (iBLF) was proposed. Firstly, based on the Lyapunov stability theory, the backstepping control method was adopted and iBLF was constructed to guarantee the constraints on output and state as well as the stability of the system; secondly, the RBF neural network was used to approximate the unknown nonlinear terms in the dynamic system for the unknown dynamic compensation of the DC motor system, by which the angular velocity can track the expected value quickly, the output and state are kept within the predetermined range, and the tracking error is constrained by decreasing exponentially over time; finally, the simulation experiment results further proved the effectiveness of the proposed control method.