Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Adaptive Parameter Control Using AAN for Lower Limb Rehabilitation Exoskeletons

Zheng Sun, Wenkong Wang, Zizhong Wei, Xin Ma

TL;DR

The paper addresses the challenge of adaptable, precise control in lower-limb exoskeletons for rehabilitation. It develops an assist-as-needed framework combining a human–exoskeleton coupling model, a human torque-momentum observer (HTMO), and an adaptive parameter controller (APC), with torque estimation via inverse dynamics and a spring–damper interaction. The HTMO provides patient-torque estimates under model uncertainties, while the APC delivers nonlinear, adaptive torque/trajectory control. Through MATLAB/Simulink simulations, the approach demonstrates reduced tracking errors (≈0.02 rad) and smoother interaction torques, outperforming LQR and AANSMC controllers and showing strong potential for patient-specific rehabilitation.

Abstract

Exoskeletons play a crucial role in assisting patients with varying mobility levels during rehabilitation. However, existing control strategies face challenges such as imprecise trajectory tracking, interaction torque oscillations, and limited adaptability to diverse patient conditions. To address these issues, this paper proposes an assist-as-needed (AAN) control algorithm that integrates a human-robot coupling dynamics model, a human torque-momentum observer (HTMO), and an adaptive parameter controller (APC). The algorithm first employs inverse dynamics to compute the joint torques required for the rehabilitation trajectory. The HTMO then estimates the torque exerted by the patient's joints and determines the torque error, which the exoskeleton compensates for via a spring-damper system, ultimately generating the target trajectory. Finally, the APC ensures adaptive assistive control. The proposed method is validated for its effectiveness in MATLAB/Simulink.

Adaptive Parameter Control Using AAN for Lower Limb Rehabilitation Exoskeletons

TL;DR

The paper addresses the challenge of adaptable, precise control in lower-limb exoskeletons for rehabilitation. It develops an assist-as-needed framework combining a human–exoskeleton coupling model, a human torque-momentum observer (HTMO), and an adaptive parameter controller (APC), with torque estimation via inverse dynamics and a spring–damper interaction. The HTMO provides patient-torque estimates under model uncertainties, while the APC delivers nonlinear, adaptive torque/trajectory control. Through MATLAB/Simulink simulations, the approach demonstrates reduced tracking errors (≈0.02 rad) and smoother interaction torques, outperforming LQR and AANSMC controllers and showing strong potential for patient-specific rehabilitation.

Abstract

Exoskeletons play a crucial role in assisting patients with varying mobility levels during rehabilitation. However, existing control strategies face challenges such as imprecise trajectory tracking, interaction torque oscillations, and limited adaptability to diverse patient conditions. To address these issues, this paper proposes an assist-as-needed (AAN) control algorithm that integrates a human-robot coupling dynamics model, a human torque-momentum observer (HTMO), and an adaptive parameter controller (APC). The algorithm first employs inverse dynamics to compute the joint torques required for the rehabilitation trajectory. The HTMO then estimates the torque exerted by the patient's joints and determines the torque error, which the exoskeleton compensates for via a spring-damper system, ultimately generating the target trajectory. Finally, the APC ensures adaptive assistive control. The proposed method is validated for its effectiveness in MATLAB/Simulink.

Paper Structure

This paper contains 11 sections, 33 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Human/Exoskeleton model
  4. Coupling model of interaction forces
  5. Main Results
  6. HTMO Design
  7. APC Design
  8. Simulation and discussion
  9. Proposed controller
  10. Contrast Controllers
  11. Conclusion

Figures (7)

  • Figure 1: Human-machine coupled dynamics system
  • Figure 2: System control logic block diagram
  • Figure 3: HTMO when the human muscle moment is 35%, 70%, 100% of the target torque. upper: hip, lower: knee.
  • Figure 4: Trajectory tracking chart of the wearer under different torques provided by the patient. left: hip, right: knee.
  • Figure 5: Trajectory/interaction torque tracking curves under different controllers with 35% of patients providing torque. left: hip, right: knee.
  • ...and 2 more figures