通过在风扰环境中不断强化学习的四旋翼轨迹跟踪控制器
Yanhui Liu1, Lina Hao1, Shuopeng Wang1
1School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了持续强化学习框架,以改善四旋翼在不断变化的风条件下的轨迹跟踪. 与标准算法相比,该方法提高了适应性并减少了错误.
科学领域:
- 机器人技术
- 人工智能
- 控制系统
背景情况:
- 四旋翼轨迹跟踪精度在动态风场下降, 挑战传统的控制器.
- 现有数据驱动的方法面临着灾难性的遗忘,
- 对于复杂的环境任务来说, 强大的控制在变化的风条件下至关重要.
研究的目的:
- 开发一个强化学习框架,不断适应在动态风场中强大的四旋翼追踪.
- 解决处理风力干扰的传统和数据驱动方法的局限性.
- 提高四旋翼机的环境适应性和跟踪性能.
主要方法:
- 一个持续强化学习框架,整合持续反向传播和强化学习.
- 在没有风的条件下进行初始训练,然后通过实用性评估进行动态神经元重置.
- 提高训练精度和效率的多目标奖励功能.
- 使用 Gazebo/PX4 模拟平台进行验证,并使用渐进式和随机风变化.
主要成果:
- 与标准的近距离政策优化 (PPO) 算法相比,该算法的轨迹跟踪中等平方误差有所减少.
- 通过结构化神经元重置成功解决了深度强化学习中的可塑性损失问题.
- 在动态风场中显著提高四旋翼的持续适应能力.
结论:
- 拟议的框架提供了一个强大的解决方案,用于在具有挑战性和时间变化的风力干扰中跟踪四旋翼轨迹.
- 通过结构化神经元重置的持续适应维持了网络的可塑性并提高了性能.
- 这种方法提高了四旋翼在复杂的环境任务中的可靠性.
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