Intelligent adaptive backstepping H_∞ tracking control system for a DSP-based PMSM servo drive

This paper proposes an intelligent adaptive backstepping H_∞ tracking control system (IABHTCS) for the position control of permanent-magnet synchronous motor (PMSM) servo drive. The IABHTCS 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 to stabilize the PMSM servo drive system. However, particular information about the uncertainties of the PMSM servo drive is required in the ideal backstepping control law so that the corresponding control performance can not influenced seriously. To relax the requirement for the value of the lumped uncertainty in the backstepping controller, an adaptive DRFWNN uncertainty observer is designed to adaptively estimate the non-linear uncertainties online. 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 IABHTCS can be guaranteed. Finally, a computer simulation is developed and an experimental system is established to testify the effectiveness of the proposed IABHTCS. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulation and experimental results confirm that the proposed IABHTCS can achieve favorable tracking performance regardless of parameters uncertainties by incorporating DRFWNN identifier, backstepping control and H_∞ control technique.