Adaptive RCMAC Neural Network Dynamic Surface Control for Permanent-Magnet Synchronous Motors Driven Two-Axis X-Y Table

Abstract

In this paper, a robust adaptive dynamic surface control (RADSC) is proposed with a recurrent cerebellar model articulation controller (RCMAC) based on the function link neural network (FLNN) for two-axis X-Y table derived by two permanent-magnet synchronous motors (PMSMs) servo drives. Initially, an optimal computed torque controller (OCTC) is used to ensure the stability of the two-axis X-Y table system. But there may be destroyed in the control performance due to the presence of parameter uncertainties. This might be due to the fact that linear optimal control possesses inherent robustness within a specified spectrum of model uncertainties. Due to the above reasons, the RADSC is developed according to the system requirements in order to increase the robustness of the control system. In the proposed control scheme, a dynamic surface controller (DSC) and an RCMAC uncertainty observer with a robust controller are combined to constitute the RADSC scheme. In the proposed RADSC, the DSC is utilized to get rid of the complexity of explosion present in the backstepping design. Furthermore, the RCMAC uncertainty observer is developed such that it can approximate the nonlinear parameter uncertainty terms online, whereas the robust controller is designed to recover the residual of the approximation error of the RCMAC. Based on the Lyapunov stability analysis, the online adaptive control laws are derived. The experimental results confirm that the x-axis and y-axis motions are controlled separately, whereas it can be concluded that the proposed RADSC's dynamic behaviors accomplish robust tracking performance though there were parameter uncertainties.