Aug 2026· Journal of Robotics and Mechatronics· 0 citations· 21 references
TL;DR
This study significantly reduces the gait trajectory tracking errors of joint actuators in a lower-limb rehabilitation robot, thereby providing a feasible and effective approach for the optimization of its control algorithm design.
Abstract
Lower-limb rehabilitation robots are valuable for gait training, but accurate joint motor angle tracking remains challenging due to various motion-related disturbances. This paper presents a staged joint-compensation strategy to improve control accuracy. The gait control process is partitioned into initiation, cyclic, and termination phases. A multilayer perceptron is employed during initiation and termination to predict and compensate for short-term aperiodic errors, while a Transformer-based sequence model combined with repetitive-control concepts is used in the cyclic phase to predict and correct periodic errors. Phase detection and safety-constraint mechanisms are integrated to ensure system stability and safety. Experiments are performed on a self-developed robotic platform with field-oriented control at the motor level, using a 165 cm, 60 kg dummy as the load. The proposed strategy substantially reduced joint-angle RMSE: left hip from 0.692° to 0.494° (28.6% reduction), right hip from 0.687° to 0.402° (41.5% reduction), left knee from 1.754° to 0.426° (75.7% reduction), and right knee from 1.667° to 0.461° (72.3% reduction). Ablation studies and repeated-trial statistical analyses further confirm the effectiveness of the approach. This study significantly reduces the gait trajectory tracking errors of joint actuators in a lower-limb rehabilitation robot, thereby providing a feasible and effective approach for the optimization of its control algorithm design.
Intelligent lower-limb exoskeletons are gradually supplanting traditional rehabilitation equipment and have become a prominent research focus. However, existing systems still face gait compliance challenges due to various disturbances during actual walking. To address this, we propose a novel RBF neural network-based control method. The network approximates the uncertainties induced by disturbances in the patient's lower-limb musculature, enabling adaptive parameter tuning, and we concurrently introduce a new dynamic stability assessment approach. By analysed the principles and inherent limitations of the conventional zero-moment point stability criterion during the late single-leg support phase, the proposed strategy enhances both the stability of gait motion and the smoothness of the trajectory, thereby satisfying the real-time demands of rehabilitation training. Theoretical analysis and comparative experiments confirm the stability and reliability of the exoskeleton's walking posture, demonstrating that the method meets the control requirements for rehabilitation robots while ensuring patient safety.
Mo-Yao Gao, Yue-Xi Zhong, Bo-Yan Wu et al.· 2026 5th International Sympo...· 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
Against the background of an aging population, addressing post-stroke upper limb hemiplegia rehabilitation needs and the high costs, inconsistent efficacy of traditional training, this paper proposes a rehabilitation robot design integrating LSTM and sliding mode control (SMC). Upper limb rehabilitation robots, as nonlinear systems, face external disturbances; traditional SMC, though robust, requires high switching gains, leading to chattering that impairs application effects. To solve this, the study first defines upper limb joint motion characteristics and safe control ranges as the kinematic basis, then introduces LSTM to capture disturbance temporal features and realize predictive modeling of time-varying disturbances. LSTM-predicted disturbances are used as feedforward compensation in the SMC law, combined with saturation functions to reduce switching gains and suppress chattering. The solution retains SMC’s robustness, improves rehabilitation precision and comfort, meets upper limb motion requirements, and provides a feasible technical approach for robot-assisted rehabilitation of post-stroke upper limb hemiplegia.
Fucai Zhao, Pengfei Wang, Liang Zheng et al.· 2026 IEEE International Conf...· 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.
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
Accurate actuator-space tracking is an important engineering requirement for repeatable motion delivery by parallel rehabilitation mechanisms, but controller performance should also be assessed under configuration-dependent dynamics and modeling uncertainty. This study evaluates a bounded fuzzy-supervised proportional–integral–derivative (FSPID) controller for a three-chain 3STC+S parallel rehabilitation mechanism. CAD-derived inverse kinematics generated the three prismatic-joint reference trajectories, while closed-loop behavior was simulated using a Simscape Multibody model including rigid-body mass and inertia properties, gravity, closed-loop constraints, and external force/moment loading. Three fixed-gain PID loops formed the baseline. The FSPID supervisor used zero-order Sugeno inference to adjust proportional and derivative gains within prescribed bounds, while the integral action remained fixed and was implemented with a leaky integrator. Under the nominal 1° trajectory at 0.2 Hz, FSPID reduced mean root-mean-square error (RMSE), maximum absolute error, and integral absolute error (IAE) by 0.25%, 0.37%, and 0.21%, respectively. Under sustained external loading, maximum-error reductions reached 9.33–10.20%, with mean actuator-wise peak-force changes within approximately ±2%. Additional simulations examined trajectory amplitude, synthetic measurement noise, viscous damping, and combined nonidealities. The results support FSPID as a conservative simulation-level refinement of fixed-gain PID; experimental validation remains necessary.
A. Banyai, Daniel-Vasile Banyai, C. Brisan· Bioengineering· 0 citations
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