具有神经网络估计器的非线性多代理系统的预定义时间分布式优化和反干扰控制:一个层次框架
Haitao Wang1, Qingshan Liu1, Chentao Xu2
1School of Mathematics, Southeast University, Nanjing 210096, China.
概括
本研究引入了对非线性多代理系统的层次控制,在预定义的时间内实现最佳共识,尽管存在未知的功能和干扰. 该方法确保了机器人手臂和移动机器人的准确轨迹跟踪.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 分布式优化对于多代理系统至关重要.
- 处理未知的非线性和干扰是一个重大挑战.
- 预先定义的时间控制比传统方法提供了更快的融合.
研究的目的:
- 为非线性多代理系统开发预定义时间分布式优化策略.
- 为了应对未知的非线性函数和外部干扰所带来的挑战.
- 确保最佳的共识轨迹和准确的系统跟踪在固定的时间内.
主要方法:
- 建议建立一个双层层次的层次控制框架.
- 一个预定义的时间分布式估计器产生最佳的共识轨迹.
- 神经网络近似未知的非线性,一个干扰观察者估计外部干扰.
- 基于神经网络的防干扰滑动模式控制器确保了轨迹跟踪.
主要成果:
- 拟议的层次控制框架保证了预先定义的时间趋同和稳定性,通过利亚普诺夫分析进行验证.
- 系统轨迹在预定义的时间内成功追踪最佳轨迹.
- 在机器人手臂和移动机器人模型上的模拟证明了该方法的有效性.
结论:
- 提出的层次控制方法有效地实现了对非线性多代理系统的预定义时间分布式优化.
- 神经网络和干扰观察者的集成增强了对不确定性的稳定性.
- 该方法为机器人技术中复杂的多代理协调任务提供了可靠的解决方案.
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