Aug 2026· 2026 IEEE International Conference on Mechatronics and Automation (ICMA)· pp. 2102-2107· 0 citations· 22 references
Abstract
To address the coupling between ambiguous sEMG-based intention recognition and mechanical safety constraints in cable-driven lower-limb rehabilitation, this study proposes a multimodal perception-based adaptive admittance control framework. Mechanical, interaction, and physiological indicators, including stiffness, end-effector position, coupling force, velocity, joint motion, and sEMG, were fused to estimate active participation, passive/flaccid tracking, and spasticity risk. The identified state was then used to adjust admittance parameters online, while an RBFNN-SMC inner loop improved trajectory tracking. Experiments showed high-damping, low-stiffness protection during spasticity bursts and milder responses during passive conditions. Compared with PID, RBFNN-SMC reduced tracking errors, supporting safer adaptive rehabilitation control.
Home-based rehabilitation exoskeletons often suffer from control instability due to low-cost force sensors. This paper presents a robust, sensorless Composite Variable Impedance Control architecture that separates trajectory tracking (virtual stiffness K) from active assistance (adaptive feedforward torque τassist). By eliminating high-frequency force feedback, the system ensures intrinsic stability. Experiments on the CURE platform demonstrate independent modulation of compliance (RMSE 2.64° to 15.90°) and effective assistance during simulated weakness, reducing tracking RMSE from 13.77° to 3.12°. Results show τassist contributes 59.6% of total torque, enabling "High-Assistance, High-Compliance" interaction without reactive stiffening. This provides a stable execution layer for advanced, bio-signal-driven "Assist-as-Needed" (AAN) therapies.
Jun Leng, Pengcheng Li, Hanze Wang et al.· 2026 IEEE International Conf...· 0 citations
This study proposes a composite control framework for upper-limb rehabilitation exoskeletons integrating fractional-order impedance control, computed-torque compensation, and a momentum-based nonlinear disturbance observer. By combining Jacobian-transpose force mapping, fractional-order impedance shaping, and observer-based compensation, the framework enhances compliant yielding and tracking robustness under non-ideal dynamics. Simulation results show that, under severe spasticity-like impact, interaction force remains within 45 N. Under 30% mass mismatch and strong friction, tracking accuracy improves without reaching the 60 N·m torque limit. Under tremor-like disturbance, 4-8 Hz oscillations are attenuated while low-frequency voluntary motion is preserved, improving compliance, disturbance rejection, and torque smoothness.
Chun Liu, Xinrong Liu, Yuhang Xue· Computer Methods in Biomecha...· 0 citations
This paper presents a wrist rehabilitation robot based on a 3R spherical linkage and proposes a multimodal human–robot interaction (HRI) control method integrating surface electromyography (sEMG) and force sensing. The proposed robot provides three degrees of freedom (DoFs) of posture adjustment for wrist rehabilitation training and incorporates a 6-axis force sensor to establish an interactive rehabilitation platform. A variable admittance control strategy is further developed using fuzzy rules. In this strategy, sEMG signals are used to characterize muscle activation and active participation, while interaction force/torque information reflects the current mechanical interaction state. Based on these multimodal inputs, the admittance damping parameter is adjusted online. Experimental results show that the proposed method can identify different training states, including active exertion, abnormal spasticity, muscle weakness, and interaction discomfort. Compared with the fixed admittance controller, the physical-signal-based admittance controller, and the physiological-signal-based variable admittance controller, the proposed controller increases muscle activation by 5.88%, 4.79%, and 1.07% of the full-scale range, respectively, thereby demonstrating superior effectiveness in promoting active participation.
Zeyi Huang, Qi-Zhou Guo, Qing-Lei Wang et al.· 2026 IEEE International Conf...· 0 citations
Knee rehabilitation robots can effectively assist the recovery of patients with lower limb motor dysfunction. This paper presents a lightweight single-drive adaptive knee rehabilitation robot for supine training. The robot consists of a waist support, a thigh mechanism, and a shank-foot mechanism, and provides continuous adjustment of thigh length, shank length, and foot support angle to improve geometric matching for users with different body sizes and postures. To reduce the influence of the device’s self-weight on training, a gravity compensation method based on kinematic coupling is developed, and a sensorless interaction torque estimation method based on motor current feedback is introduced. Using only a single encoder and motor current feedback, the system achieves passive, active-assistive, and resistive behaviors within a unified control framework, without explicit mode switching. Experimental results show that, in active-assist mode, the initial response delay is about 240.0 ms, the trajectory correlation coefficient reaches 0.9960, and the maximum position error is 13.48 mm. In admittance-based interaction control mode, the estimated interaction torque agrees well with the measured torque, with a Pearson correlation coefficient of 0.954, and the average volunteer rating under six impedance levels remains above 3.7/5. These results demonstrate that the proposed robot achieves good adaptability, stable tracking, and reliable human-robot interaction, providing a practical solution for low-cost and integrated knee rehabilitation.
Mei Feng, Xiang Chen, Chao Han et al.· 2026 IEEE International Conf...· 0 citations
Lower-limb rehabilitation exoskeletons have emerged as a promising complementary technology to conventional therapy, enabling repetitive, intensive, and personalized gait training. However, achieving accurate trajectory tracking while maintaining robustness against parametric uncertainties, external disturbances, and unpredictable human–robot interaction remains a significant control challenge due to the highly nonlinear dynamics of coupled human–exoskeleton systems. This paper presents an experimental performance comparison of five control strategies for gait rehabilitation exoskeletons, including a classical proportional–integral–derivative (PID) controller, a model-based proportional–derivative controller with gravity compensation (PD+G), a computed torque sliding mode controller (CT-SMC), a computed torque–super-twisting sliding mode controller (CT–ST-SMC) and a hybrid backstepping–super-twisting sliding mode controller (BS–ST-SMC). All the controllers were implemented on the same lower-limb rehabilitation exoskeleton under identical operating conditions. The experimental results demonstrate that the proposed BS–ST-SMC architecture outperforms classical and traditional robust approaches, particularly in mitigating chattering and managing human–robot interaction uncertainties. Specifically, the BS–ST-SMC achieved the highest tracking precision with a mean squared position error (MSEp) of 1.32×10−3rad2 and effectively synchronized with the user by reducing the phase lag to just 4.22° at the knee joint. Their overall performance was evaluated using the following metrics: mean squared position error (MSEP), mean squared velocity error (MSEv), peak error, phase lag, jerk index, peak torque, and peak power.
Yukio Rosales-Luengas, Sergio Salazar, Saúl J. Rangel-Popoca et al.· Electronics· 0 citations
The findings indicate that integrating reinforcement learning with Sigmoid parameter adaptation provides a systematic and effective solution for adaptive compliance regulation in mobile exoskeleton systems, enhancing adaptability, safety, and functional relevance for stroke patients undergoing lower limb rehabilitation training.
