Hierarchical multiloop MPC scheme for robot manipulators with nonlinear disturbance observer.

Area of Science:

• Robotics
• Control Systems Engineering
• Mechatronics

Background:

• Robot manipulators require precise control for complex tasks.
• Traditional control methods struggle with external disturbances and model uncertainties.
• Enhancing robustness is crucial for reliable robotic system operation.

Purpose of the Study:

• To propose a novel hierarchical multiloop model predictive control (MPC) scheme.
• To improve the robustness and accuracy of robot manipulator trajectory tracking.
• To address control challenges posed by external disturbances and system uncertainties.

Main Methods:

• Implemented inverse dynamics-based feedback linearization to decouple robot manipulator dynamics.
• Integrated a nonlinear disturbance observer (NDO) for uncertainty and disturbance compensation.
• Developed a hierarchical multiloop MPC framework combining these techniques.

Main Results:

• The proposed scheme effectively decoupled the multi-link robot manipulator, reducing computational load.
• The NDO successfully compensated for external disturbances and uncertainties, enhancing controller robustness.
• Simulations on a 3-DOF robot manipulator demonstrated comparative accuracy and superior robustness.

Conclusions:

• The hierarchical multiloop MPC scheme offers an effective solution for robust robot manipulator control.
• The integration of feedback linearization and NDO significantly improves trajectory tracking performance.
• This approach advances the state-of-the-art for controlling robotic systems in uncertain environments.