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Summary
This summary is machine-generated.

This study introduces adaptive fault-tolerant control for robot manipulators using synchronization and neural networks. The proposed method enhances robot performance and fault resilience, outperforming traditional techniques.

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adaptive controlfault-tolerant controlneural networkpassive fault-tolerant controlrobot manipulatorsynchronization

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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.