通过强化学习来控制四旋翼团队与积极领导者的最佳封闭控制
IEEE transactions on cybernetics
|June 27, 2023
概括
本研究介绍了一种最佳控制器,用于控制任务中的多个未经调节的四旋翼. 控制器确保稳定的制和机动性,通过合作运输任务的模拟进行验证.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能和机器学习
背景情况:
- 四旋翼系统面临的挑战是由于低功率,非线性和外部干扰.
- 封闭控制任务需要精确的协调和机动性,特别是与多个代理商.
- 积极的领导者可以提高四旋翼团队的集体性能和机动性.
研究的目的:
- 为一支执行封闭控制任务的低精度四旋翼团队提出一个最佳控制器.
- 解决四旋翼系统中非线性动力学,不确定性和外部干扰所带来的挑战.
- 为了利用活跃的领导者,在封闭系统内提高机动性.
主要方法:
- 开发一个复合控制器,具有不同的位置和态度控制规律.
- 使用非政策的强化学习来训练使用历史四旋翼轨迹数据的控制法律.
- 理论分析以保证闭环系统的稳定性.
主要成果:
- 拟议的控制器有效地实现了四旋翼团队的位置控制.
- 态度控制法成功调节了旋转运动.
- 模拟结果证实了控制器在多个活跃领导者的合作运输任务中的有效性.
结论:
- 开发的最佳控制器对于在控制任务中未能执行的四旋翼机组是有效的.
- 强化学习为四旋翼系统学习复杂的控制规律提供了一种可行的方法.
- 积极领导者的整合提高了整体系统的性能和机动性.
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