Predefined-Time Dynamic Self-Triggered Approximate Optimal Control of Autonomous Surface Vehicles With Disturbances

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence
• Marine Engineering

Background:

• Autonomous Surface Vehicles (ASVs) require robust control strategies to navigate effectively in dynamic environments.
• Disturbances and computational limitations pose significant challenges for real-time motion control of ASVs.
• Existing control methods often lack efficiency in terms of computational and communication burden.

Purpose of the Study:

• To develop a predefined-time optimal motion control strategy for ASVs facing disturbances.
• To implement a dynamic self-triggered framework to reduce computational and communication overhead.
• To ensure stable ASV motion within a specified time frame using reinforcement learning.

Main Methods:

• Formulation of a predefined-time second-order integral sliding mode control (SOISM) strategy.
• Development of a predefined-time approximate optimal motion (AOM) control strategy using a single critic network.
• Application of a dynamic self-triggered framework for adaptive control updates.
• Design of novel Lyapunov functions and triggered conditions for stability analysis.

Main Results:

• The proposed SOISM and AOM control strategies effectively eliminate disturbance influence on ASVs.
• The dynamic self-triggered framework significantly reduces computational and communication burdens.
• Stability of the sliding mode dynamics and the disturbed ASV is proven within a specified time frame.
• Simulation results confirm the efficacy of the developed motion control approach.

Conclusions:

• The integrated control approach ensures stable and optimal motion for ASVs under disturbances.
• The dynamic self-triggered framework enhances control system efficiency.
• This research provides a robust and computationally efficient solution for ASV motion control.