Extended state observer based dynamic iterative learning for trajectory tracking control of a six-degrees-of-freedom...

Area of Science:

• Robotics
• Control Systems Engineering
• Automation

Background:

• Industrial automation relies on precise manipulator control.
• Existing trajectory tracking schemes face challenges like high coupling, complex calculations, and poor robustness.
• Multiple-jointed robotic manipulators require advanced control for accuracy in repetitive tasks.

Purpose of the Study:

• To address low accuracy and poor robustness in robotic manipulator trajectory tracking.
• To propose an advanced control strategy for six-degrees-of-freedom (six-DOF) manipulators.
• To overcome uncertainties in repetitive industrial tasks.

Main Methods:

• Iterative learning control (ILC) combined with model compensation (MC) and an extended state observer (ESO).
• ESO estimates external and internal model perturbations and uncertainties.
• ILC enables fast and accurate control for repetitive tasks, while MC simplifies inverse operations.

Main Results:

• The proposed control scheme demonstrates enhanced precision and robustness for manipulator trajectory tracking.
• Convergence of the control scheme is mathematically proven using Lyapunov function and time-varying approximation theory.
• Simulation and experimental results validate the effectiveness of the proposed strategy.

Conclusions:

• The integrated ESO, ILC, and MC strategy offers a robust solution for precise trajectory tracking in robotic manipulators.
• This approach effectively handles model uncertainties and external disturbances in industrial automation.
• The validated scheme provides a reliable method for improving the performance of six-DOF manipulators.