分布强大的基于学习的倒退控制,辅助神经动力学,形成共识,跟踪水下船只
IEEE transactions on cybernetics
|August 16, 2023
概括
本研究引入了基于学习的强有力的多个水下船舶的控制,以实现共识形成跟踪,尽管未知参数和干扰. 开发的协议通过在线学习和神经动力学来确保稳定和适应性性能.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 海洋工程 海洋工程
- 人工智能的人工智能
背景情况:
- 在复杂的任务中,对多个水下车辆的协调控制至关重要.
- 未知的系统参数,建模错误和环境干扰带来了重大挑战.
- 现有的控制方法往往难以实时适应不确定性.
研究的目的:
- 开发一个基于学习的分布式强大的控制协议,用于对多艘水下船只的共识形成跟踪.
- 为了解决未知的系统参数,建模不匹配,海洋干扰和噪音.
- 在不确定的水下环境中确保稳定性和适应性性能.
主要方法:
- 利用图形理论进行分布式控制器合成,并保证稳定性.
- 采用后退控制技术来处理动态模型中的参数不确定性.
- 开发了一种在线学习程序,并纳入了一个神经动力学模型,以应对不确定性和干扰.
主要成果:
- 成功合成了一个分布式控制器,确保了水下船舶形成跟踪的稳定性.
- 在线学习和神经动力学模型有效地解决了时间变化的系统,建模错误和环境干扰.
- 理论稳定性分析证实了闭环系统的强大的适应性性能.
结论:
- 拟议的基于学习的分布式强大的控制协议是有效的共识形成跟踪多个水下船舶.
- 该方法在存在未知的参数和复杂的环境条件下提供了稳定和适应性的解决方案.
- 模拟实验验证了开发的控制策略的有效性和稳定性.
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