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Realizing asynchronous finite-time robust tracking control of switched flight vehicles by using nonfragile deep
Haoyu Cheng1, Ruijia Song2, Haoran Li1
1Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an, China.
Frontiers in Neuroscience
|January 8, 2024
Summary
A new nonfragile deep reinforcement learning (DRL) method enhances control for switched unmanned flight vehicles. This approach improves accuracy and robustness by combining DRL with robust control techniques for finite-time stability.
Area of Science:
- Aerospace Engineering
- Control Systems
- Artificial Intelligence
Background:
- Switched unmanned flight vehicles require advanced control for accuracy and robustness.
- Conventional control methods face challenges with model uncertainties and transient performance.
- Deep Reinforcement Learning (DRL) offers potential for intelligent control but requires careful design for stability.
Purpose of the Study:
- To propose a novel nonfragile deep reinforcement learning (DRL) method for finite-time control of switched unmanned flight vehicles.
- To enhance control accuracy, robustness, and intelligence by integrating conventional robust control with DRL.
- To ensure finite-time stability under asynchronous switching using advanced analytical techniques.
Main Methods:
- A hybrid tracking controller combining a dynamics-based controller (using linear matrix inequalities) and a learning-based controller (using DRL).
- Finite-time stability analysis employing multiple Lyapunov functions and mode-dependent average dwell time.
- Online optimization formulated as a Markov decision process, solved with an adaptive deep deterministic policy gradient algorithm.
- Incorporation of nonfragile control theory and an adaptive reward function within the DRL framework.
Main Results:
- The proposed DRL method effectively compensates for model uncertainties and improves transient control accuracy.
- Comparative simulations demonstrated the superior effectiveness of the presented algorithm over conventional approaches.
- The combined analytical techniques confirmed the finite-time stability of the flight vehicles with asynchronous switching.
Conclusions:
- The novel nonfragile DRL approach provides enhanced control performance for switched unmanned flight vehicles.
- The integration of robust control and DRL offers a powerful strategy for improving system accuracy and stability.
- The developed algorithm achieves excellent stability and training efficiency, paving the way for more intelligent autonomous systems.
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