以分布式神经动力学为基础的倒退,为强大的受约束共识提供最佳控制
IEEE transactions on cybernetics
|August 21, 2023
概括
本研究提出了一个新的控制策略,用于舰队的无损水下车辆 (UUVs),以实现强大的形成跟踪在3D空间,克服复杂的约束和干扰.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 海洋工程 海洋工程
背景情况:
- 在3D空间中,对未经调节的水下车辆 (UUV) 的形成跟踪控制带来了重大挑战.
- 现有的方法经常与非全方位的约束和低效率作斗争.
研究的目的:
- 开发一种新的基于共识的最佳协调协议,以及用于UUV舰队形成跟踪的强大的控制器.
- 解决UUV控制中的非全方位约束和低执行问题.
主要方法:
- 采用了一个层次控制架构.
- 顶层使用球形坐标转换来处理约束和分布式最佳运动协调.
- 下层采用基于神经动力学的强大的后退控制器,避免了"术语爆炸"问题.
主要成果:
- 在满足约束条件的同时,为UUV舰队实现了最佳的形成跟踪.
- 成功处理了下调和未知的干扰.
- 证明了改进的控制活动,避免了传统的后退问题.
结论:
- 拟议的层次控制策略确保了UUV车队的强有力的受约束形成跟踪.
- 系统稳定性已被证明,所有UUV状态在干扰下最终均界限.
- 模拟结果验证了开发协议的优越性和有效性.
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