Sliding-Mode-Based Model Predictive Control for Enhancing Robustness and Output Performance in Dynamic Inductive Power Transfer Systems

In dynamic inductive power transfer (DIPT) systems for automated guided vehicles (AGVs), the movement of the AGV can significantly impact the coupling coefficient, degrading the output performance. Conventional control methods, while capable of addressing this challenge, often exhibit weak robustness due to their sensitivity to circuit parameter variations and require a trial-and-error process for tuning coefficients, thus increasing design complexity and difficulty. This article proposes a sliding-mode-based model predictive control (SM-MPC) approach tailored for DIPT systems to enhance robustness and output performance. The SM-MPC employs a predictive model to force the control object to converge to a sliding surface, leveraging fast dynamic response characteristics to mitigate AGV movement-induced disturbances. A pole-placement method is introduced to directly acquire the optimal sliding coefficient, eliminating iterative tuning requirements and simplifying the design process. Herein, the DIPT system model is presented, followed by the SM-MPC design methodology, stability analysis, and sliding coefficient design. Finally, simulations and experimental results validate the proposed SM-MPC’s effectiveness in stabilizing the output performance of DIPT systems, demonstrating improved robustness against parameter variations and disturbances caused by AGV movement compared to existing control methods.