Iterative Learning Adaptive Active Damping Based DC-Link Capacitor Ripple Current Suppression Strategy for PMSM Drives

Fixed damping strategies are inadequate for suppressing the dc-link capacitor ripple current in permanent magnet synchronous motor (PMSM) drives across a wide power range. Therefore, a ripple current suppression strategy based on adaptive active damping with iterative learning is proposed. The strategy indirectly characterizes the ripple current by the dc-link voltage, avoiding the need for additional sensors or current reconstruction algorithms. First, the limitation of fixed damping in suppressing the ripple current is revealed using the equivalent admittance model. Then, an adaptive suppression mechanism based on P-type iterative learning is constructed. The balance between the convergence speed and the disturbance rejection capability is achieved by introducing a forgetting factor. On this basis, the initial admittance parameters are dynamically matched to improve the iterative efficiency under variable-speed conditions. The analysis of convergence and parameter sensitivity demonstrates that the proposed strategy exhibits rapid convergence, high accuracy, and strong robustness. Finally, the effectiveness of the proposed strategy is verified on the experimental platform.