ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs.

Area of Science:

• Robotics
• Control Systems Engineering
• Ocean Engineering

Background:

• Uncrewed Underwater Vehicles (UUVs) face significant challenges in 3D trajectory tracking due to uncertain dynamics and unpredictable ocean disturbances.
• Existing control methods often struggle to provide robust performance under these complex conditions.

Purpose of the Study:

• To develop a robust self-learning control scheme for UUVs that effectively addresses uncertain dynamics and time-varying ocean disturbances.
• To enhance the 3D trajectory tracking accuracy and overall control performance of underactuated UUVs.

Main Methods:

• Utilizing radial basis function neural networks to simplify complex uncertainties into a manageable parametric form.
• Implementing an actor-critic neural network structure based on action-dependent heuristic dynamic programming (ADHDP) for adaptive control.
• Designing a self-learning control scheme to optimize performance index functions and adapt to system variations.

Main Results:

• The proposed ADHDP-based control scheme demonstrates robust trajectory tracking for UUVs.
• Theoretical analysis confirms that all signals within the closed-loop control system remain bounded.
• Simulation results validate the effectiveness and superior performance of the developed control strategy.

Conclusions:

• The ADHDP-based robust self-learning control scheme offers a viable solution for UUV trajectory tracking under uncertainty.
• The approach ensures good robustness and control performance, making UUV operations more reliable.
• The method provides a foundation for advanced autonomous control of underwater systems.