Parameter optimization of type II fuzzy sliding mode control for bridge crane systems based on improved grey wolf algorithm

Bridge cranes are complex nonlinear dynamic systems with underactuated characteristics, making it challenging for controllers to man age the spatial swing of the load effectively. Additionally, uncertainties both within and outside the system adversely impact control performance. To address these issues, a Type-II fuzzy sliding mode controller has proven effective in enhancing the anti-swing control performance of the payload. However, due to the intricate parameter adjustment optimization problem and potential challenges in dealing with nonlinearity and uncertainty, especially in complex dynamic systems, this paper proposes a grey wolf algorithm based on a dynamic spiral hunting mechanism. This enhancement endows the algorithm with improved convergence speed and higher robustness, enabling more effective parameter tuning for the second-order fractional-order sliding mode controller (FSMC). The proposed algorithm demonstrates superior convergence speed and solution accuracy performance through testing and comparison. Finally, simulation verification under two conditions of the bridge crane system validates the effectiveness of the proposed approach. This paper proposes a grey wolf algorithm with a dynamic spiral hunting mechanism to enhance parameter tuning for the second-order fractional-order sliding mode controller (FSMC) in bridge crane systems. It addresses challenges in nonlinear dynamic systems, improving convergence speed and robustness. Testing shows superior performance in convergence speed and solution accuracy. Simulation verification confirms the effectiveness of the proposed approach in anti-swing control of bridge crane payloads, crucial for managing spatial swing under uncertainties. image