Aug 2026· Design for Augmented Humanity· 1 citation· 33 references
TL;DR
Design, modeling, and experimental validation of a self-reconfigurable parallel ankle rehabilitation platform demonstrate the approach’s feasibility, demonstrating stable trajectory tracking and setpoint regulation under practical conditions, with minor deviations attributable to actuator and mechanical nonlinearities.
Abstract
This paper presents design, modeling, and experimental validation of a self-reconfigurable parallel ankle rehabilitation platform. The proposed system employs a three-limb parallel mechanism with mechanically reconfigurable joints, enabling seamless transition between static mobilization and dynamic gait-oriented operation while preserving alignment with the anatomical ankle joint center. A position-level inverse kinematics formulation is developed to directly compute actuator lengths from desired platform orientation, eliminating drift associated with velocity-integration approaches. The corresponding Jacobian matrix is derived to describe the velocity relationship and is evaluated over the rehabilitation workspace to ensure nonsingular operation. A closed-loop control framework based on Jacobian mapping and Lyapunov-guided design is implemented for stable motion tracking. Simulation results demonstrate accurate tracking of multi-axis trajectories with limited cross-axis coupling. Experimental validation on a physical prototype confirms the approach’s feasibility, demonstrating stable trajectory tracking and setpoint regulation under practical conditions, with minor deviations attributable to actuator and mechanical nonlinearities. The results highlight the potential of the proposed self-reconfigurable parallel architecture for anatomically consistent, adaptive ankle rehabilitation in both clinical and home-based settings.
With the increasing aging population and the growing prevalence of upper-limb motor impairments, conventional rehabilitation approaches face limitations in terms of labor demand and long-term training continuity. This study presents a shape memory alloy (SMA)-driven upper-limb rehabilitation exoskeleton for assisting elbow and wrist joint motions. The system integrates an SMA actuator with a Bowden cable transmission mechanism to achieve a lightweight and compliant actuation design for human–robot interaction. A nonlinear SMA model based on the Liang–Rogers formulation is adopted to describe phase transformation behavior and hysteresis characteristics. A PWM-based PID control strategy was implemented to regulate joint motion and improve tracking performance under the inherent thermal delay of SMA actuation. The proposed system is evaluated through MATLAB/Simulink simulations and experimental testing. The results demonstrate that the exoskeleton can achieve stable trajectory tracking for elbow and wrist rehabilitation motions. The study confirms the feasibility of SMA-based lightweight rehabilitation systems while highlighting the influence of SMA thermal dynamics on system performance.
Sunke Zhu, Mingwei Liu, Lei Wang et al.· Engineering Research Express· 0 citations
To enhance the efficacy and safety of rehabilitation, this study presents a bionic ankle rehabilitation mechanism integrated with a posture-switching function. The device utilizes a parallelogram mechanism to address the challenges of sit-to-stand (STS) transition assistance. By synergizing a dual crank-linkage with a three-link parallel mechanism, the device accurately replicates natural gait trajectories and multi-dimensional ankle movements. In terms of kinematic analysis, an inverse kinematic model incorporating spatial rotation matrices was established to derive the nonlinear relationship between actuator inputs and platform pose outputs. Furthermore, instantaneous kinematic equations were extracted, providing a direct theoretical basis for subsequent rehabilitation verification. Finite element analysis (FEA) demonstrates that under an extreme load of 1500 N, the peak stress of the core structure 211.08 MPa and the resulting deformation remain well below the material’s failure thresholds. These results validate the structural reliability and clinical potential of the proposed mechanical design.
Kun Liu, Jialun He, Xiaodong Huang et al.· 2026 IEEE International Conf...· 0 citations
Improving trajectory-following accuracy remains a central issue in lower-limb rehabilitation exoskeletons, especially when joint motion is affected by nonlinear coupling, parameter drift, and interaction disturbances during repetitive gait training. To address this problem, this study develops an adaptive trajectory-tracking method driven by an RBF neural network. A single exoskeleton leg is first represented as a two-degree-of-freedom planar double-link mechanism in the sagittal plane, and the swing-phase dynamics are formulated via the Lagrange approach. On this basis, a nominal-model decomposition framework is introduced so that uncertain dynamics and external perturbations can be approximated online by the RBF network. A joint-space controller is then constructed to improve tracking stability and robustness. MATLAB simulations and prototype experiments are further carried out, with conventional PID control used for comparison. The results indicate that the proposed approach yields faster error attenuation, smaller steady-state oscillation, along with improved resistance to disturbances in the hip and knee motions. Overall, the proposed approach is effective for accurate gait-following control in rehabilitation exoskeletons.
Yuhui Yang, Chao Yang, Yuanxiang Guo et al.· International Conference on...· 0 citations
This paper presents a feasibility study of an active ankle assist mechanism based on a four-link model representing single-leg stance dynamics. A dynamic model with two actuated ankle joints is derived, and an invariant-manifold-based nonlinear stabilization controller is designed. For practical motor implementation, three control schemes are formulated and compared: Ideal Torque control, Current-Based Torque control, and Angle-Based control with realistic motor current and position loops. Simulation results demonstrate successful stabilization under external disturbances for all implementations. The motor torque scheme closely reproduces the ideal torque benchmark and maintains stability over a wide feedback gain range. The angle-based scheme achieves comparable transient disturbance suppression but exhibits different robustness margins under feedback gain variations due to structural differences in gravity compensation and input realization. Frequency-domain analysis further reveals similar low-frequency disturbance attenuation and distinct higher-frequency gain characteristics across the implementations. These results confirm the feasibility of motor-based implementation and clarify the structural trade-offs between torque and angle realizations toward practical wearable assistive systems.
Kotone Takano, Jun Ishikawa· 2026 IEEE/ASME International...· 0 citations
In order to improve the trajectory tracking accuracy of the lower limb rehabilitation exoskeleton robot, this paper proposes an adaptive robust error compensation control method (ARCEC) based on RBF neural network. First, the dynamics of the single-leg swing phase of the lower-limb exoskeleton are modeled using Lagrange’s equations, taking into account factors such as joint friction, flexible transmission, and the torque arising from human–robot interaction. Subsequently, nominal model compensation, online approximation via RBF neural networks, and nonlinear error feedback are integrated to mitigate the impact of model uncertainty and external disturbances on trajectory tracking performance. Simulation and experimental results demonstrate that compared to traditional PID control and sliding mode control (SMC), the ARCEC method exhibits significant advantages in trajectory tracking accuracy. It achieves up to a 30% reduction in tracking error, enabling the lower-limb exoskeleton to precisely track human gait curves.
Chao Yang, Xin Han, Zhijue Huang et al.· International Conference on...· 0 citations
This study develops a Two-Degree-of-Freedom (2-DoF) lower-limb exoskeleton for rehabilitation applications. The system combines trajectory tracking control with a force-feedback control system to improve human–exoskeleton interaction during rehabilitation movements. Real-time force information is incorporated into the control framework to adjust joint motion according to the interaction conditions between the user and the exoskeleton. A kinematic and dynamic model of the system is established and evaluated through MATLAB simulation. Experimental evaluations are conducted using both single- and dual-force sensor configurations. The results show that the dual-force sensor configuration provides enhanced motion stability and lower oscillatory behavior compared with the single-force sensor configuration. The integration of trajectory tracking control and force-feedback control contributes to smoother movement, enhanced stability, and better rehabilitation performance. These results support the application of force-based interaction control in lower-limb rehabilitation exoskeletons.
Khin Yadana Kyaw, D. Maneetham, Myo Min Aung· Engineering, Technology &...· 0 citations
