State estimation and dynamic compensation cooperative sliding mode control for space flexible manipulators

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Space flexible manipulator systems face challenges with total interference suppression.
• Uncertainties and unmeasurable states degrade system performance and tracking accuracy.

Purpose of the Study:

• To propose a state estimation and dynamic compensation cooperative sliding mode control strategy.
• To enhance interference suppression, tracking accuracy, and robustness in space flexible manipulator systems.

Main Methods:

• Utilized a neural network-based disturbance observer for real-time estimation and compensation of composite uncertainties.
• Implemented a sliding mode observer to reconstruct unmeasurable system states.
• Designed a nonsingular terminal sliding mode control law for finite-time convergence.

Main Results:

• The proposed strategy effectively estimates and compensates for system uncertainties.
• Accurate reconstruction of unknown states was achieved using the sliding mode observer.
• The cooperative sliding mode controller demonstrated improved tracking accuracy and robustness against disturbances.
• Simulations confirmed excellent dynamic performance and stability, even with nonlinear factors like friction.

Conclusions:

• The developed cooperative sliding mode control strategy successfully addresses interference suppression and state estimation challenges in space flexible manipulators.
• The approach significantly improves system robustness and tracking accuracy, particularly in scenarios with partially unmeasurable states.
• This method offers a viable solution for enhancing the performance of space robotic systems operating under complex conditions.