Disturbance Observer-Based Fault-Tolerant Control for Robotic Systems With Guaranteed Prescribed Performance

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Robotic systems often face dynamic uncertainties and actuator failures, compromising performance.
• Partial Loss of Effectiveness (PLOE) and Total Loss of Effectiveness (TLOE) are critical failure modes.
• Existing control methods may struggle to simultaneously address uncertainties and diverse actuator failures.

Purpose of the Study:

• To develop a robust control strategy for robotic systems experiencing actuator failures.
• To compensate for both Partial Loss of Effectiveness (PLOE) and Total Loss of Effectiveness (TLOE).
• To guarantee prescribed performance bounds under system uncertainties and unknown disturbances.

Main Methods:

• A disturbance observer (DO) utilizing neural networks was designed to mitigate unknown disturbances.
• Control design incorporated barrier Lyapunov functions (BLFs) to handle time-varying constraints.
• The approach was validated through simulations on a two-link planar manipulator.

Main Results:

• The proposed controllers effectively compensated for actuator failures (PLOE and TLOE).
• Prescribed performance, system uncertainties, and unknown disturbances were managed simultaneously.
• The control strategy demonstrated robustness in simulation and experimental verification.

Conclusions:

• The developed control method offers a viable solution for actuator failure compensation in uncertain robotic systems.
• The integration of neural network-based DO and BLF ensures reliable system performance.
• Experimental validation on a Baxter robot confirms the practical applicability of the proposed controllers.