Sequential-synchronized optimized tracking control for a stratospheric airship with reinforcement learning

Area of Science:

• Aerospace Engineering
• Control Systems
• Robotics

Background:

• Stratospheric airships require advanced control for safe and efficient trajectory tracking.
• Existing control methods struggle with the coupled fast-slow dynamics of airship attitude and position.
• Energy consumption is a critical constraint for long-duration missions.

Purpose of the Study:

• To develop a novel control scheme for stratospheric airships that improves trajectory tracking safety and efficiency.
• To address the challenge of fast-slow dynamics in airship control systems.
• To minimize energy consumption while maintaining high tracking performance.

Main Methods:

• A sequential-synchronized optimized control (SSOC) scheme is proposed.
• The scheme synchronizes attitude state convergence first, followed by position state convergence.
• Reinforcement learning is combined with optimal control theory to optimize energy usage.

Main Results:

• The SSOC scheme ensures system states converge in a predefined sequence, accommodating fast-slow dynamics.
• Theoretical proofs confirm the sequential-synchronized convergence and overall system stability.
• Simulations demonstrate the effectiveness of the SSOC strategy compared to other methods.

Conclusions:

• The proposed SSOC scheme effectively enhances the safety and efficiency of stratospheric airship trajectory tracking.
• The approach successfully manages complex system dynamics and optimizes energy consumption.
• This control strategy offers a promising solution for future stratospheric airship missions.