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This is a Experimental Study study (also classified as: Quantitative Study).

October 16, 2025Open Access

Application of Hybrid SMA (Slime Mould Algorithm)-Fuzzy PID Control in Hip Joint Trajectory Tracking of Lower-Limb Exoskeletons in Multi-Terrain Environments

WLWei LiUniversity of Vermont XWXiaojie WeiNanning Normal University DSDongxian SunBeijing Institute of Technology

Key Points

The proposed hybrid control strategy significantly reduced adjustment time by up to 61.9%, improving tracking performance.
A dynamic model using the Lagrange method facilitated the design of a fuzzy PID controller for enhanced adaptability.
Integration of the slime mould algorithm optimized parameters, lowering maximum error by up to 72.81% compared to conventional approaches.
Experimental tests across various terrains highlighted substantial improvements in response speed and system stability.

Abstract

This paper addresses the challenges of inadequate trajectory tracking accuracy and limited parameter adaptability encountered by hip joints in lower-limb exoskeletons operating across multi-terrain environments. To mitigate these issues, we propose a hybrid control strategy that synergistically combines the slime mould algorithm (SMA) with fuzzy PID control, thereby improving the trajectory tracking performance in such diverse conditions. Initially, we established a dynamic model of the hip joint in the sagittal plane utilizing the Lagrange method, which elucidates the underlying motion mechanisms involved. Subsequently, we designed a fuzzy PID controller that facilitates dynamic parameter adjustment. The integration of the slime mould algorithm (SMA) allows for the optimization of both the quantization factor and the proportional factor of the fuzzy PID controller, culminating in the development of a hybrid control framework that significantly enhances parameter adaptability. Ultimately, we performed a comparative analysis of this hybrid control strategy against conventional PID, fuzzy PID, and PSO-fuzzy PID controls through MATLABR2023b/Simulink simulations as well as experimental tests across a range of multi-terrain scenarios including flat ground, inclines, and stair climbing. The results indicate that in comparison to PID, fuzzy PID, and PSO-fuzzy PID controls, our proposed strategy significantly reduced the adjustment time by 15.06% to 61.9% and minimized the maximum error by 39.44% to 72.81% across various terrains including flat ground, slope navigation, and stair climbing scenarios. Additionally, it lowered the steady-state error ranges by an impressive 50.67% to 90.75%. This enhancement markedly improved the system’s response speed, tracking accuracy, and stability, thereby offering a robust solution for the practical application of lower-limb exoskeletons.

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