Fractional-Order Sliding-Mode Control and Radial Basis Function Neural Network Adaptive Damping Passivity-Based Control with Application to Modular Multilevel Converters

Abstract

This paper proposes a hybrid control scheme that combines fractional-order sliding-mode control (FOSMC) with radial basis function neural network adaptive damping passivity-based control (RBFPBC) for modular multilevel converters (MMC) under non-ideal operating conditions. According to the passive control theory, we establish the Euler–Lagrange (EL) models of positive and negative sequences based on the unbalanced grid. A passivity-based controller that satisfies the energy dissipation law is designed. To enable rapid convergence of the system energy storage function, a radial basis function neural network (RBFNN) is introduced to adjust the injection damping adaptively. Additionally, a fractional-order sliding-mode controller (FOSMC) is designed. The fractional-order sliding mode surface used can improve tracking performance, and effectively suppressed the undesirable chattering phenomenon compared to the traditional sliding-mode control (SMC). Finally, combining the two control methods can effectively solve the issue of passivity-based control (PBC) being too dependent on parameters. The proposed hybrid control scheme enhances the ability of the system to resist disturbances, and improves its overall robustness. Simulation results demonstrate the feasibility and effectiveness of this control method.

Keywords

• modular multilevel converters
• RBF neural network
• fractional-order sliding-mode control
• passive control