广东省基础与应用基础研究基金(2019A1515110352); 季华实验室开放课题(X190021TB194); 科技创新2025重大专项(2020Z073)
无人机控制器的设计开发是一项复杂的系统工程, 传统的基于代码编程的开发方式存在开发难度大、周期长及错误率高等缺点. 同时, 强化学习智能飞控算法虽在仿真中取得很好的性能, 但在实际中仍缺乏一套完备的开发系统. 本文提出一套基于模型的智能飞控开发系统, 使用模块化编程及自动代码生成技术, 将强化学习算法应用于飞控的嵌入式开发与部署. 该系统可以实现强化学习算法的训练仿真、测试及硬件部署, 旨在提升以强化学习为代表的智能控制算法的部署速度, 同时降低智能飞行控制系统的开发难度.
The design and development of unmanned aerial vehicle (UAV) controllers are complex system engineering. The traditional development method based on code programming has the disadvantages of difficult development, long cycle, and high error rate. Although the intelligent flight control algorithm based on reinforcement learning has achieved good performance in simulation, it still lacks a complete development system in practice. This study presents a model-based development system for intelligent flight control, applying the reinforcement learning algorithm to the embedded development and deployment for flight control with modular programming and automatic code generation technologies. The system is equipped for the training simulation, testing, and hardware deployment of the reinforcement learning algorithm, and it is expected to improve the deployment speed of intelligent control algorithms represented by reinforcement learning and to reduce the development difficulty of intelligent flight control systems.