Prescribed Performance Speed Control Without Initial Condition Restrictions for Asynchronous Motor Drive Systems

Asynchronous motors are widely used in electric vehicle drive systems because of their simple structure, low cost, and high reliability. Accurate speed tracking and smooth transient response are important during start-up, acceleration, and deceleration. However, sensing uncertainties and sensor faults may affect the measured signals and reduce control performance. In this study, an adaptive prescribed performance control (PPC) method is developed for asynchronous motor speed regulation. A nonlinear mapping and an improved tangent-type barrier Lyapunov function (BLF) are used to remove the requirement that the initial tracking error must lie within the prescribed performance bounds. Radial basis function neural networks are used to approximate the unknown nonlinear terms. The stability analysis shows that all closed-loop signals remain bounded and that the tracking error enters and remains within the prescribed performance region after the initial expansion stage. Simulations under different initial motor speeds, the considered sensor-fault conditions, and load disturbances are conducted. Under the adopted comparative conditions, the proposed method reduces the convergence time, steady-state error, maximum tracking error, and recovery time by 69.5%, 93.4%, 92.2%, and 49.4%, respectively. The results show that the proposed method improves the transient response, tracking accuracy, and disturbance recovery of the asynchronous motor drive system.