Research on adaptive robust control of lower limb rehabilitation exoskeleton

Adaptive SMC for Lower Limb Rehabilitation Robots Using a Sliding Mode Hyperbolic ESO

Performance Comparison of Classical and Robust Control Strategies for a Lower-Limb Rehabilitation Exoskeleton

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.

Assist-As-Needed Backstepping Control of Lower-Limb Exoskeletons with Human Effort Estimation and Comparative Evaluation Against Sliding Mode and PID Controllers

In this paper, we propose an assist-as-needed (AAN) backstepping control scheme for a lower-limb exoskeleton with nonlinear dynamics and uncertain human–robot interactions. The main objective is to achieve a good trajectory tracking capability while adaptively controlling the assistance of the robot according to the user’s effort. The adopted dynamic model is nonlinear, which includes joint dynamics and external human interaction torque. This allows for the derivation of the tracking error formulation. The backstepping control law, formulated based on the filtered tracking error, ensures stable closed-loop performance with bounded tracking errors. We incorporate an AAN scaling framework based on estimated human effort to regulate the overall control torque as a convex combination of the nominal backstepping torque and the impedance-based assistance torque. The proposed controller was tested by numerical simulations and was compared with the sliding mode control (SMC) and the proportional–integral–derivative (PID) control. The overall root-mean-square tracking error for the proposed controller was 0.0962 rad, while for the SMC controller and PID controller, it was 0.0819 rad and 0.1246 rad, respectively. Moreover, the proposed controller reduced the peak human–robot interaction torque to 14.68 N·m compared to 15.36 N·m for SMC and 15.81 N·m for PID, adaptively controlling assistance based on the applied effort of the user. The assistance ratio went down from an average of 0.7988 in the low-effort condition to 0.6960 in the higher-effort condition, indicating effective adaptation while maintaining stable tracking performance. Although the PID controller achieved the lowest torque-variation index, the proposed controller achieved a more favorable trade-off among tracking accuracy, adaptive assistance, and acceptable torque smoothness. Finally, the proposed AAN backstepping controller achieved a practical trade-off between tracking accuracy, adaptive assistance, torque smoothness, and interaction safety, suggesting its potential in rehabilitation and assistive exoskeleton applications.

Design and Analysis of a 2-DoF Exoskeleton Utilizing Force Sensors for Adaptive Trajectory Tracking

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.  

Dynamics-Based Collaborative Control for an Exoskeleton-Walker System via Deterministic Learning

Lower limb exoskeletons and mobile robots hold great potential in improving motor function rehabilitation for patients with limb dysfunction. However, their widespread application is limited by the substantial time and effort investment required from professional rehabilitation therapists. A significant challenge in achieving autonomous and intelligent rehabilitation lies in addressing the coordinated control between the exoskeleton and the robotic walker. This paper proposes a novel collaborative learning control strategy based on deterministic learning, which aims to achieve high-performance coordinated control through precise closed-loop dynamics modeling of the exoskeleton-walker system. First, radial basis function neural networks (RBFNNs) are employed to approximate the system dynamics during the coordinated control process. Utilizing deterministic learning theory, it is rigorously demonstrated that, under persistent excitation conditions, the unknown dynamics of the system can be accurately approximated and stored as constant neural networks. Subsequently, an experience-based collaborative learning controller is designed, enabling autonomous coordinated control of the human-robot system and offering a viable approach for its broader application. The effectiveness and superior performance of the proposed control strategy are validated through experiments conducted on the CoppeliaSim robotic platform. Note to Practitioners—This work is intended for researchers and engineers working on rehabilitation robotics, particularly those focusing on lower limb exoskeletons and mobile robotic walkers. One of the main barriers to the practical deployment of these systems is the reliance on continuous assistance from rehabilitation professionals during use. To address this, we propose a deterministic learning-based collaborative control strategy that enables accurate modeling and reuse of system dynamics, ultimately achieving autonomous coordination between the exoskeleton and robotic walker. This reduces reliance on manual intervention and opens up the possibility for long-term, high-performance rehabilitation training in clinical and home environments. Practitioners can leverage this approach to develop smarter, more adaptive rehabilitation systems that respond to patient needs with greater precision and autonomy.

Optimization of Knee Exoskeleton Rehabilitation Trajectory Based on Iterative Learning

In the process of knee rehabilitation, the traditional control method has a fixed trajectory and cannot balance the early high-precision tracking and the later human-machine compliance. This paper proposes an iterative learning control (ILC) scheme based on the dynamic adjustment mechanism of phased learning rate. To begin with, A single degree-of-freedom kinetic model of the knee exoskeleton was constructed using the subject's anatomical data. Subsequently, this paper designs a stepwise renewal law that uses a high learning rate to accelerate the establishment of motor memory during the passive rehabilitation period, and a stepwise downward adjustment of the learning rate during the active rehabilitation period to enhance the compliance of the system to the body's active force. The simulation results show that the fluctuation of root mean square error (RMSE) under active interference is much smaller than that of the traditional fixed gain algorithm. Microscopic analysis further confirmed that the strategy could effectively inhibit the torque distortion caused by human-machine confrontation, and while ensuring the stability of the rehabilitation trajectory, it gave the system good physical flexibility, which provided theoretical support for full-cycle personalized rehabilitation.