Observer-Based Prescribed Performance Speed Control for PMSMs: A Data-Driven RBF Neural Network Approach

In this article, an observer-based prescribed performance speed control method is proposed for permanent magnet synchronous motors. A transformed speed error is introduced and a suitable controller is designed to make it converge to zero, while guaranteeing the original speed error evolves strictly within a prescribed region. The controller is designed based on a backstepping approach. A linear extended state observer is applied to estimate and feed forward the external constant load disturbance to improve robustness. A data-driven radial-basis function neural network is proposed to approximate the nonlinear dynamic caused by parameter uncertainties and periodic-changing disturbance by deploying real-time and historical data. The stability analysis is based on Lyapunov's control theory. Experimental results verify the effectiveness and advantages of the proposed control scheme.