基于神经网络的预定义时间双方形成跟踪控制不确定的异质欧勒-拉格朗奇系统在任务空间的跟踪控制
Xiao-Yu Zhang1, Tao Han1, Bo Xiao1
1School of Electrical Engineering and Automation, Hubei Normal University, Huangshi 435005, PR China.
ISA transactions
|March 20, 2024
概括
本研究介绍了对不确定的欧勒-拉格朗日系统的等级控制算法,以实现预定义时间的双部分形成跟踪. 该方法确保了复杂的机器人系统的更快的融合和适应性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 形成跟踪对于多代理系统至关重要.
- 欧勒-拉格朗日系统中的不确定性带来了重要的控制挑战.
- 预定义的时间控制比传统方法提供了更快的趋同.
研究的目的:
- 为解决不确定的异质欧勒-拉格朗日系统的任务空间双方形成跟踪问题.
- 开发一种能够在预定义的时间内实现跟踪的等级控制算法.
- 为了提高系统的稳定性,应对动态不确定性.
主要方法:
- 一个分层的预定义时间控制算法的设计.
- 使用非单一的滑动表面,可调节的沉降时间.
- 整合一个辐射基础功能神经网络来处理系统的不确定性.
- 开发一个领导状态估计器和一个特定于阵营的控制器.
主要成果:
- 拟议的算法成功实现了任务空间双方形成跟踪.
- 非单一的滑动表面允许灵活调节沉时间.
- 射线基函数神经网络有效地减轻动态不确定性.
- 数字模拟验证了算法的有效性和实际适用性.
结论:
- 开发的分层预定义时间控制算法对不确定的欧勒-拉格朗系统有效.
- 这种方法提供了精确和可适应的训练跟踪能力.
- 神经网络的集成提高了复杂的动态环境中的稳定性.
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