A fault-tolerant and robust controller using model predictive path integral control for free-flying space robots.

Area of Science:

• Robotics and Autonomous Systems
• Space Engineering
• Control Theory

Background:

• Space missions increasingly utilize robotic manipulators for tasks like servicing, assembly, and debris removal.
• Autonomous operation is critical for space robots due to reachability limitations and harsh operating conditions, including faults and uncertainties.

Purpose of the Study:

• To develop and evaluate a novel robust control technique for space manipulators.
• To address challenges posed by parameter uncertainties and component failures in autonomous space operations.

Main Methods:

• Introduction of the Planner-Estimator Model Predictive Path Integral Control (PE-MPPI) algorithm.
• PE-MPPI integrates a planner for system control and an estimator for adapting to parameter uncertainties.
• Performance evaluation in the context of a debris removal mission's pre-capture phase, simulating uncertainties and failures.

Main Results:

• The proposed PE-MPPI controller demonstrates superior performance compared to the standard Model Predictive Path Integral Control (MPPI).
• Effective handling of parameter uncertainties and system component failures was confirmed through simulations.
• Robustness of the PE-MPPI algorithm in challenging space mission scenarios was validated.

Conclusions:

• PE-MPPI offers a robust and effective control solution for autonomous space manipulators operating under uncertain conditions.
• The developed technique enhances the reliability and performance of robots in critical space missions like debris removal.
• This approach represents a significant advancement in autonomous control for space robotics.