Research on fuzzy hierarchical sliding mode anti-sway control of bridge crane based on optimization of MGO algorithm

Abstract

Bridge cranes are widely used lifting equipment in factories, ports, and construction sites. To address the problem of excessive load swing during bridge crane operations, a fuzzy hierarchical sliding mode control (HSMC) anti-swing strategy optimized using the Moss Growth Optimization (MGO) algorithm is proposed. First, a single-pendulum dynamic model of the bridge crane system is established. Subsequently, the MGO algorithm is introduced, followed by the design of the fuzzy hierarchical sliding mode controller. The MGO algorithm is then employed to optimize the controller parameters. A stability analysis of the closed-loop system is carried out, and comparative studies as well as robustness experiments are conducted on the MATLAB/Simulink platform. The experimental results demonstrate that the proposed MGO-based fuzzy hierarchical sliding mode controller outperforms the Multiple Sliding Mode (MSM) and Linear Quadratic Regulator (LQR) controllers in terms of load swing suppression and trolley positioning speed. Furthermore, the proposed controller exhibits excellent and stable control performance under varying operating conditions, effectively suppressing load swing while achieving fast and accurate trolley positioning, thereby demonstrating strong robustness and adaptability.