Adaptive sliding mode controller based on improved Sparrow search algorithm for tracking control of human lower limb...

Area of Science:

• Robotics
• Control Systems
• Biomedical Engineering

Background:

• Lower-limb assistive exoskeletons require advanced control for effective human-robot interaction.
• Existing controllers face challenges with user variability, external disturbances, and model uncertainties.
• Adaptive control strategies are crucial for real-time performance enhancement.

Purpose of the Study:

• To present an Improved Sparrow Search Algorithm-Adaptive Sliding Mode Controller (ISSA-SMC) for accurate motion tracking in lower-limb assistive exoskeletons.
• To enhance controller adaptability and robustness by integrating human joint torque inputs.
• To optimize controller parameters for improved performance and reduced computational load.

Main Methods:

• Developed an ISSA-SMC by integrating human joint torque inputs into the exoskeleton's dynamic model.
• Employed a softmax strategy and branch and bound method for efficient parameter optimization.
• Validated the controller's effectiveness through simulations and physical experiments on a human-worn exoskeleton.

Main Results:

• ISSA-SMC demonstrated superior tracking accuracy and robustness compared to conventional Sliding Mode Control (SMC) and Adaptive Fuzzy Sliding Mode Control (AFSMC).
• The proposed controller significantly reduced overshoot, steady-state error, and response time.
• Achieved real-time adaptation to user variability, external disturbances, and modeling uncertainties with low computational cost.

Conclusions:

• The ISSA-SMC offers a practical, optimization-driven solution for wearable robotics.
• This framework provides valuable insights into intelligent rehabilitation and adaptive human-robot interaction.
• The study highlights the potential of advanced control algorithms for enhancing exoskeleton performance.