Sliding-Mode Nonlinear Predictive Control of Brain-Controlled Mobile Robots.

Area of Science:

• Robotics
• Control Systems
• Neuroscience

Background:

• Brain-controlled robots (BCRs) offer advanced human-robot interaction.
• Ensuring safety and robustness in BCRs remains a significant challenge.
• Existing controllers may not adequately balance human intention tracking with safety constraints.

Purpose of the Study:

• To develop a robust sliding-mode nonlinear predictive controller for brain-controlled robots.
• To enhance the performance, safety, and robustness of BCRs.
• To minimize interference with human intention while guaranteeing robot safety.

Main Methods:

• Developed kinematics and dynamics models for a mobile robot.
• Designed a cascaded controller integrating a predictive controller and a smooth sliding-mode controller.
• Formulated an optimization problem for the predictive controller to balance human intention and safety.
• Designed a smooth sliding-mode controller for robust velocity tracking.

Main Results:

• Human-in-the-loop simulations demonstrated controller efficacy.
• Robotic experiments confirmed robust performance and enhanced safety.
• The controller successfully minimized invasion to human intention.
• Achieved robust desired velocity tracking.

Conclusions:

• The proposed sliding-mode nonlinear predictive controller significantly enhances BCR performance and safety.
• This controller provides a robust design for future brain-controlled robot development.
• The approach effectively integrates human intention tracking with critical safety guarantees.