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Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand

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Adaptive-Robust Controller for Smart Exoskeleton Robot.

Brahim Brahmi¹, Hicham Dahani¹, Soraya Bououden²

¹Electrical Engineering Department, College Ahuntsic, Montreal, QC H2M 1Y8, Canada.

Sensors (Basel, Switzerland)

|January 23, 2024

Summary

This summary is machine-generated.

This study introduces a new adaptive control method for rehabilitation robots, enhancing precision and robustness. The advanced control framework effectively manages complex robot dynamics for improved patient outcomes.

Keywords:

adaptive control exoskeleton robot function approximation technique robust control unknown dynamics

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Area of Science:

Robotics
Control Systems Engineering
Biomedical Engineering

Background:

Rehabilitation robotics offers significant potential for individuals with disabilities.
Complex and uncertain dynamics in these systems pose control challenges.
Advanced control strategies are crucial for effective rehabilitation robot operation.

Purpose of the Study:

To develop a novel adaptive control framework for rehabilitation robots.
To address challenges posed by unknown system dynamics and uncertainties.
To achieve precise tracking, robustness, fast response, and reduced chattering.

Main Methods:

Integration of modified function approximation (MFAT) and double-integral non-singular terminal sliding mode control (DINTSMC).
Inclusion of a higher-order sliding mode observer to eliminate velocity feedback requirements.
Validation through simulations and experimental testing on an exoskeleton robot.

Main Results:

The proposed adaptive control framework demonstrated effective handling of unknown system dynamics.
Precise tracking performance, high robustness, and fast response were achieved.
Stability analysis confirmed the closed-loop system's uniform ultimate boundedness via Lyapunov theory.

Conclusions:

The novel adaptive control framework provides an effective solution for controlling rehabilitation robots.
The approach enhances accuracy and robustness, overcoming inherent system variations.
This advancement is expected to improve rehabilitation robot control and patient outcomes.