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Robust LQR-Based Neural-Fuzzy Tracking Control for a Lower Limb Exoskeleton System with Parametric Uncertainties and

Jyotindra Narayan1, Santosha K Dwivedy1

  • 1Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India.

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A robust neural-fuzzy control scheme enhances lower limb exoskeleton gait training by managing uncertain dynamics and external disturbances. This adaptive system ensures stable, accurate passive-assist rehabilitation for improved patient outcomes.

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Area of Science:

  • Robotics and Control Systems
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Lower limb exoskeleton control faces challenges from uncertain dynamics and human reflexes during gait rehabilitation.
  • Accurate control is crucial for effective passive-assist gait training.

Purpose of the Study:

  • To propose a robust linear quadratic regulator-based neural-fuzzy (RLQR-NF) control scheme.
  • To address payload uncertainties and external disturbances in lower limb exoskeleton systems.
  • To ensure stable and accurate gait tracking during passive-assist rehabilitation.

Main Methods:

  • Established nonlinear dynamics using the Euler-Lagrange principle.
  • Employed input-output feedback linearization to achieve a linearized state-space form.
  • Developed an adaptive neuro-fuzzy inference system (ANFIS) with offline dataset formulation and online adaptation for disturbance estimation.
  • Utilized Lyapunov theory to guarantee system stability.

Main Results:

  • The RLQR-NF controller demonstrated promising gait tracking performance.
  • Comparative analysis showed superior robustness against parametric uncertainties (up to 30% mass increase) and external disturbances compared to traditional LQR and exponential reaching law-based sliding mode (ERL-SM) controllers.
  • Ensured asymptotic stability of the human-exoskeleton system.

Conclusions:

  • The proposed RLQR-NF control scheme effectively manages uncertainties and disturbances in lower limb exoskeleton systems.
  • This robust control strategy is well-suited for passive-assist gait training applications.
  • The findings highlight the potential of advanced control techniques for enhancing rehabilitation robotics.