High-Precision Intelligent Adaptive Backstepping H∞ Control for PMSM Servo Drive Using Dynamic Recurrent Fuzzy-Wavelet- Neural-Network

This paper proposes a high-precision intelligent adaptive backstepping control system (HPIABCS) for the position control of permanent-magnet synchronous motor (PMSM) servo drive. The HPIABCS incorporates an ideal backstepping controller, a dynamic recurrent-fuzzy-wavelet-neural-network (DRFWNN) uncertainty observer and a robust H∞ controller. First, a backstepping position controller is designed and analyzed. Furthermore, to relax the requirement of the lumped uncertainty, an adaptive DRFWNN uncertainty observer is used to adaptively estimate the non-linear uncertainties online, yielding a controller that tolerate a wider range of uncertainties. In addition, the robust controller is designed to achieve H∞ tracking performance to recover the residual of the approximation error and external disturbances with desired attenuation level. The online adaptive control laws are derived based on the Lyapunov stability analysis, the Taylor linearization technique and H∞ control theory, so that the stability of the HPIABCS can be guaranteed. Finally, a computer simulation is developed and an experimental system is established to testify the effectiveness of the proposed HPIABCS. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulation and experimental results confirm that the proposed HPIABCS can achieve favorable tracking performance regardless of parameters uncertainties by incorporating DRFWNN identifier, backstepping control and H∞ control technique. Key-Words: Adaptive control, backstepping control, permanent-magnet synchronous motor (PMSM), dynamic recurrent-fuzzy-wavelet-neural-network (DRFWNN), Lyapunov stability theorem, H∞ control.