Investigation of Trajectory Tracking Control in Hip Joints of Lower-Limb Exoskeletons Using SSA-Fuzzy PID...

Area of Science:

• Robotics
• Control Systems Engineering
• Biomedical Engineering

Background:

• Lower-limb exoskeleton robots are increasingly used in rehabilitation, requiring precise control for effective movement tracking.
• Optimizing control accuracy and response speed is crucial for enhancing rehabilitation outcomes.
• Traditional parameter tuning for controllers is often inefficient and time-consuming.

Purpose of the Study:

• To develop and validate an optimized control strategy for lower-limb exoskeleton hip robots.
• To improve trajectory tracking performance by integrating the Sparrow Search Algorithm (SSA) with fuzzy Proportional-Integral-Derivative (PID) control.
• To address the challenges of manual parameter tuning and enhance both control accuracy and response speed.

Main Methods:

• Developed a dynamic model of the hip joint using the Lagrangian method to handle complex, nonlinear dynamics.
• Integrated the Sparrow Search Algorithm (SSA) for online self-tuning optimization of fuzzy PID controller parameters (proportional and quantization factors).
• Conducted simulation and experimental studies for trajectory tracking during flat walking and standing hip flexion rehabilitation exercises.

Main Results:

• The SSA-fuzzy PID control strategy significantly improved response times by up to 30% compared to traditional PID and by up to 6% compared to fuzzy PID.
• Tracking accuracy was enhanced by up to 81.4% compared to PID and by up to 57.5% compared to fuzzy PID.
• The proposed method demonstrated superior performance in both response speed and accuracy across diverse test populations.

Conclusions:

• The SSA-fuzzy PID control strategy offers a highly effective and efficient solution for trajectory tracking in lower-limb exoskeleton robots.
• This approach provides substantial theoretical support for improving rehabilitation training for individuals with lower-limb impairments.
• The method successfully overcomes limitations of manual parameter tuning, presenting a more efficient optimization solution for exoskeleton systems.