Time-delay estimation based computed torque control with robust adaptive RBF neural network compensator for a...

Area of Science:

• Robotics
• Control Systems
• Biomedical Engineering

Background:

• Lower limb exoskeletons are crucial for gait rehabilitation.
• Existing control methods like computed torque control (CTC) have limitations in handling unmodeled dynamics and disturbances.
• Accurate and stable control is essential for effective rehabilitation outcomes.

Purpose of the Study:

• To propose a novel control approach for a 12 Degrees of Freedom (DOF) lower limb exoskeleton for gait rehabilitation.
• To enhance the accuracy and robustness of the exoskeleton's control system.
• To ensure the asymptotic stability of the proposed control strategy.

Main Methods:

• Combining time-delay estimation (TDE) based computed torque control (CTC) with robust adaptive Radial Basis Function (RBF) neural networks.
• Integrating TDE to estimate unmodeled dynamics and external disturbances.
• Designing a robust adaptive RBF neural network compensator to approximate and compensate TDE errors.
• Ensuring asymptotic stability using Lyapunov criteria.

Main Results:

• Co-simulation experiments were conducted using SolidWorks, SimMechanics, and MATLAB/Robotics Toolbox.
• The proposed controller demonstrated superior performance compared to conventional CTC, sliding mode-based CTC, and TDE-based CTC.
• Higher tracking accuracy and disturbance rejection capabilities were observed.

Conclusions:

• The proposed TDE-based CTC combined with robust adaptive RBF neural networks offers a significant advancement in exoskeleton gait rehabilitation control.
• This approach effectively addresses unmodeled dynamics and external disturbances, leading to improved control performance.
• The validated stability and superior performance highlight its potential for clinical application in gait rehabilitation.