Novel Nonlinear Backstepping Control of Synchronous Reluctance Motor Drive System for Position Tracking of Periodic Reference Inputs with Torque Ripple Consideration

Owing to air-gap field harmonic, cogging torque, stator's current time harmonic and the influence of flux saturation, a synchronous reluctance motor (SynRM) drive system has highly nonlinear uncertainties. Thus the linear control method for the SynRM drive system is difficult to achieved good performance under the nonlinear uncertainty action. To obtain better control performance the novel nonlinear backstepping control system using upper bound with switching function is firstly proposed for controlling the SynRM drive system to prevail the lumped uncertainty. With the proposed control system, the SynRM servo-drive system holds in robustness to uncertainties for the tracking of periodic reference trajectories. To enhance the robustness of the SynRM drive system, the novel nonlinear backstepping control system using adaptive law is proposed for estimating the required lumped uncertainty to reduce chattering phenomenon. When the inertia of the counterweight is varying, this proposed method can perform well in general situations, but cannot get a satisfactory performance. The novel nonlinear backstepping control system using reformed recurrent Hermite polynomial neural network with adaptive law and error estimated law is thus proposed to estimate the lumped uncertainty and compensate estimated error for obtaining better control performance. Furthermore, two varied learning rates of the reformed recurrent Hermite polynomial neural network is derived according to increment type Lyapunov function to speed-up parameter's convergence. Finally, some experimental results with comparative control performances are demonstrated, and then the effectiveness of the proposed control system with better control performance is verified for the position tracking of periodic reference inputs with torque ripple consideration.