Adaptive Fault-Tolerant Tracking Control for Multi-Joint Robot Manipulators via Neural Network-Based Synchronization

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Multi-joint robot manipulators require robust control strategies to maintain performance under fault conditions.
• Existing fault-tolerant control methods may not adequately address uncertainties and dynamic disturbances.
• Synchronization techniques offer potential for coordinated joint control and rapid error reduction.

Purpose of the Study:

• To propose an adaptive fault-tolerant control strategy for multi-joint robot manipulators.
• To enhance the robot's ability to mitigate the impact of faults and disturbances.
• To improve overall robot performance and reliability in the presence of faults.

Main Methods:

• Developing a novel robust synchronous control strategy based on terminal sliding mode control.
• Integrating neural networks for online compensation of system uncertainties, disturbances, and faults.
• Combining synchronous control with neural networks to achieve adaptive fault tolerance.

Main Results:

• The synchronization technique ensures simultaneous convergence of position errors across all joints.
• Neural networks effectively compensate for system uncertainties and adapt to faults in real-time.
• Simulations on a 3-DOF robot manipulator demonstrate superior performance compared to traditional control methods.

Conclusions:

• The proposed adaptive fault-tolerant control enhances robot manipulator resilience to faults.
• The combination of synchronous control and neural networks provides a powerful approach for fault tolerance.
• This method ensures reliable robot operation even when faults occur.