Uncertainty Compensator and Fault Estimator-Based Exponential Supertwisting Sliding-Mode Controller for a Mobile...

Area of Science:

• Robotics
• Control Systems Engineering
• Applied Mathematics

Background:

• Mobile robot path tracking is crucial for autonomous systems.
• Existing controllers face challenges with actuator limitations, faults, and uncertainties.
• Event-triggered control strategies offer potential for resource optimization.

Purpose of the Study:

• To propose a novel event-triggered exponential supertwisting algorithm (ESTA) for robust mobile robot path tracking.
• To enhance controller performance by minimizing actuator utilization and addressing uncertainties and faults.
• To validate the proposed algorithm through simulations and experimental studies.

Main Methods:

• Design of a fractional-order sliding surface-based exponential supertwisting event-triggered controller.
• Development of a fault estimator using Lyapunov stability theory to detect actuator faults.
• Integration of an integral sliding-mode controller (ISMC) with the event-triggered ESTA to filter uncertainties.

Main Results:

• The fractional-order sliding surface improved transient response, while ESTA reduced reaching time and chattering.
• The event-triggering condition, derived using the Lipschitz method, ensured minimum actuator utilization.
• The combined ESTA and ISMC approach demonstrated increased robustness against faults and uncertainties, improving tracking performance.

Conclusions:

• The proposed event-triggered ESTA offers finite-time convergence, reduced chattering, and efficient resource utilization for mobile robot path tracking.
• The controller exhibits significant robustness towards uncertainties and actuator faults.
• Simulation and experimental results confirm the superiority of the proposed method over existing techniques.