Adaptive Iterative Learning Control for Permanent Magnet Synchronous Motors with Uncertainties

This paper proposes a novel adaptive iterative learning control (AILC) strategy for position tracking of permanent magnet synchronous motor (PMSM) servo systems. The primary objective of this paper is to achieve precise position tracking control in the presence of both the load disturbances and the friction forces. The designed control scheme compensates for the load disturbance indirectly by adjusting the desired control input in the controller. Moreover, an adaptive parametric update law is employed to estimate the unknown parameters. The convergence properties of the proposed control algorithms are analyzed using the composite energy function (CEF) methodology. Additionally, the effectiveness of the proposed method for the PMSM system is validated through simulations conducted in MATLAB.