Distributed Robust Learning-Based Backstepping Control Aided With Neurodynamics for Consensus Formation Tracking of...

Area of Science:

• Robotics and Control Systems
• Marine Engineering
• Artificial Intelligence

Background:

• Coordinated control of multiple underwater vehicles is crucial for complex missions.
• Unknown system parameters, modeling errors, and environmental disturbances pose significant challenges.
• Existing control methods often struggle with real-time adaptation to uncertainties.

Purpose of the Study:

• To develop a distributed robust learning-based control protocol for consensus formation tracking of multiple underwater vessels.
• To address unknown system parameters, modeling mismatch, oceanic disturbances, and noises.
• To ensure stability and adaptive performance in uncertain underwater environments.

Main Methods:

• Utilized graph theory for distributed controller synthesis with stability guarantees.
• Employed backstepping control techniques to handle parameter uncertainties in dynamic models.
• Developed an online learning procedure and incorporated a neurodynamics model for robustness against uncertainties and disturbances.

Main Results:

• Successfully synthesized a distributed controller ensuring stability for underwater vessel formation tracking.
• The online learning and neurodynamics model effectively tackled time-varying systems, modeling errors, and environmental disturbances.
• Theoretical stability analysis confirmed robust adaptive performance of the closed-loop system.

Conclusions:

• The proposed distributed robust learning-based control protocol is effective for consensus formation tracking of multiple underwater vessels.
• The method provides a stable and adaptive solution in the presence of unknown parameters and complex environmental conditions.
• Simulation experiments validated the efficacy and robustness of the developed control strategy.