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<p>Although Pneumatic Artificial Muscles (PAMs) are recognized for their compliance and high power-to-weight ratio in ankle exoskeletons, their severe nonlinearities and hysteresis pose significant challenges for precise control, which limits the effectiveness of existing strategies. This study presents a lightweight ankle exoskeleton that not only leverages the advantages of PAMs but also introduces a novel control strategy to overcome these limitations. The key contribution is the development of a Radial Basis Function (RBF) network-optimized Sliding Mode Controller (SMC), which is specifically designed to adaptively compensate for PAM nonlinearities and time-varying disturbances. A precise dynamic model that correlates joint angle and PAM pressure is established through kinematic analysis and quasi-static testing, facilitating the controller design. Experimental evaluations on human gait demonstrate that the RBF-SMC achieves superior tracking precision compared to the conventional SMC. Specifically, for one volunteer, the average tracking error reduces from 0.71° to 0.38° (a reduction of approximately 46%), and for another, it reduces from 0.70° to 0.44° (around a 37% reduction). In addition, the proposed method significantly reduces the muscle activation levels of the pectoral and gastrocnemius muscles by 14.04% and 9.49%, respectively. These findings con/firm that the advanced RBF-SMC approach serves as a high-performance solution for achieving both precise tracking and effective muscular effort reduction in compliant robotic assistance.</p>

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Adaptive sliding mode control of a pneumatic ankle exoskeleton for gait assistance

  • 期刊论文

  • 发表日期：

    2026年5月8日

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  • Tianhong Luo,
  • Qi Tuo,
  • Yuan Li,
  • Tingqiong Cui,
  • Shuang Liang,
  • Xiangyu Ma

摘要

Although Pneumatic Artificial Muscles (PAMs) are recognized for their compliance and high power-to-weight ratio in ankle exoskeletons, their severe nonlinearities and hysteresis pose significant challenges for precise control, which limits the effectiveness of existing strategies. This study presents a lightweight ankle exoskeleton that not only leverages the advantages of PAMs but also introduces a novel control strategy to overcome these limitations. The key contribution is the development of a Radial Basis Function (RBF) network-optimized Sliding Mode Controller (SMC), which is specifically designed to adaptively compensate for PAM nonlinearities and time-varying disturbances. A precise dynamic model that correlates joint angle and PAM pressure is established through kinematic analysis and quasi-static testing, facilitating the controller design. Experimental evaluations on human gait demonstrate that the RBF-SMC achieves superior tracking precision compared to the conventional SMC. Specifically, for one volunteer, the average tracking error reduces from 0.71° to 0.38° (a reduction of approximately 46%), and for another, it reduces from 0.70° to 0.44° (around a 37% reduction). In addition, the proposed method significantly reduces the muscle activation levels of the pectoral and gastrocnemius muscles by 14.04% and 9.49%, respectively. These findings con/firm that the advanced RBF-SMC approach serves as a high-performance solution for achieving both precise tracking and effective muscular effort reduction in compliant robotic assistance.