Non-Force-Sensing Variable Admittance Control of Lower Limb Rehabilitation Exoskeleton Robots Using Class κ_∞ Function-Based Adaptive Sliding Mode

In this paper, a non-force-sensing variable admittance control approach is proposed for lower limb rehabilitation exoskeleton robots. This approach aims to provide satisfactory assistance and rehabilitation-training performance for users of such exoskeletons. Our method initiates with a novel fixed-time sliding mode observer that estimates human-machine interaction torques according to generalized momentum. This observer-based torque estimation strategy can estimate the interaction torque precisely, thereby enabling a non-force-sensing effect. Next, a variable admittance control framework is formulated for lower limb exoskeleton robots to ensure superior compliance. This framework comprises two essential components. First, a newly designed adaptive fixed-time sliding mode controller based on a class <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa _{\infty } $ </tex-math></inline-formula> function for the inner loop, which operates without requiring prior knowledge of the upper bound of lumped perturbations and guarantees precise gait trajectory-tracking performance. Second, a variable-parameter admittance model for the outer loop, which utilizes an exponential function to dynamically adjust the admittance parameters, thereby achieving a balance between the exoskeleton’s compliance and gait-correction efficacy. Finally, both simulation and experimental results are presented and analyzed to validate the effectiveness and superiority of the proposed non-force-sensing variable admittance control approach. Specifically, simulation results demonstrate that the root-mean-square (RMS) values of the inner-loop tracking errors for the proposed method are reduced by 26.1% and 20.4% at the hip and knee joints, respectively, compared with the top-performing benchmark algorithm. Meanwhile, the precision of the outer-loop observation is improved by 21% and 18% at these joints. Experimental validation further shows reductions of 14.2% and 20.1% in the RMS inner-loop tracking errors at the hip and knee joints, respectively, versus this benchmark algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i>—Motivated by the problem of how to realize effective control of lower limb exoskeleton robots to provide the wearers with appropriate comfort and gait-correction effect, this paper formulates an adaptive variable admittance control framework. In the inner loop of this framework, a novel gait trajectory-tracking controller using adaptive sliding mode is designed to ensure high tracking accuracy. In the outer loop, a new adaptive observer is designed to estimate the human-robot interaction torque, and an adaptive admittance model is formulated to provide appropriate compliance for the robot. The proposed strategies are experimentally validated and can be utilized in real-word applications. The work of this paper has reference significance for the development of lower limb exoskeleton technologies.