Optimal adaptive barrier-function super-twisting nonlinear global sliding mode scheme for trajectory tracking of...

Area of Science:

• Robotics
• Control Systems Engineering

Background:

• Parallel robots offer advantages in rigidity and load capacity over serial robots.
• Complex dynamics and uncertainties pose significant challenges for accurate parallel robot control.

Purpose of the Study:

• To develop an optimal adaptive barrier-function-based super-twisting sliding mode control scheme.
• To achieve precise trajectory tracking for parallel robots with complex dynamics, uncertainties, and external disturbances.

Main Methods:

• Utilized genetic algorithms and a global nonlinear sliding surface for controller design.
• Implemented a barrier-function-based adaptation law to eliminate the need for disturbance bound knowledge.
• Validated the controller through simulations on a Stewart manipulator and experimental tests on a 5-bar parallel robot.

Main Results:

• The proposed controller demonstrated superior tracking performance compared to PID and adaptive sliding mode control.
• The global sliding surface eliminated the reaching phase, ensuring immediate sliding mode existence.
• The barrier-function adaptation enhanced robustness against uncertainties and external disturbances.

Conclusions:

• The optimal adaptive barrier-function-based super-twisting sliding mode control scheme offers enhanced performance and robustness for parallel robots.
• This method is practical for real-world applications due to its ability to handle unknown disturbance bounds.