Robust Backstepping Control of SynRM Drive System Using Adaptive Modified Elman Neural Network Uncertainty Observer.

Due to simple construction with convex effect, the synchronous reluctance motor (SynRM) drive system has highly nonlinear uncertainties. The accurate mathematic models are difficult to be established for time-varying and nonlinear uncertainties of the actual SynRM drive system. In this paper, the robust backstepping control system using an adaptive modified Elman neural network (ENN) uncertainty observer for a SynRM drive system is proposed to achieve the better performance and robustness. Firstly, the field-oriented mechanism is applied to formulate the dynamic equation of the SynRM drive system. Secondly, the backstepping approach is proposed to control the motion of SynRM drive system. With proposed integral backstepping control system, the rotor position of the SynRM drive possesses the advantages of good control performance and robustness to uncertainties for the tracking of periodic reference trajectories. Moreover, to further increase the robustness of the SynRM drive system for nonlinear uncertainties, an adaptive modified ENN uncertainty observer is proposed to estimate the required lumped uncertainty. The on-line adaptive law of the modified ENN is derived in accordance with Lyapunov function. The updated parameters of the modified ENN are used by the gradient descent method and the backpropagation algorithm. Finally, the effectiveness of the proposed control scheme is verified by some experimental results. [ABSTRACT FROM AUTHOR]