基于神经自适应控制的完全执行系统方法,用于未执行的机器人,具有状态估计和延迟.
IEEE transactions on cybernetics
|May 2, 2025
概括
这项研究引入了一种新型适应性控制器,用于未完成的机械系统,增强了对挑战非线性动态的运动控制. 该方法可以确保精确控制操作和非操作状态,而无需线性化.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 机械工程 机械工程
- 非线性动力学是一种非线性动力学.
背景情况:
- 机械系统 (例如,海军舰艇,直升机) 由于非线性和未被调节的状态,引发了重要的运动控制挑战.
- 现有的控制方法经常与高阶动态和不可测量的状态作斗争,限制了性能和准确性.
研究的目的:
- 开发一种新的适应性控制器,用于基于完全执行系统方法的低执行系统.
- 为了应对运动控制中非线性,状态合和高顺序未执行状态所带来的挑战.
- 提供一个通用和可扩展的分析框架,用于低调的系统控制.
主要方法:
- 设计了高阶辅助变量,以将非线性未动态系统转换为线性完全动态系统,而不需要线性化.
- 雇佣了一个神经网络观察员来估计无法测量的高阶动态,提高补偿准确度.
- 开发了一种连续自适应控制器,利用完全执行的系统原则,用于未执行的机器人.
主要成果:
- 建议的辅助变量确保了非对称的收,消除了动态和非动态状态的稳定状态错误.
- 神经网络观察者有效地估计了无法测量的状态,提高了控制准确性,避免了不连续的稳定项.
- 控制器证明了对一类低效的机器人的成功应用,通过理论分析和实验验证.
结论:
- 新的自适应控制器提供了一个强大的,准确的解决方案,用于运动控制的系统.
- 该方法可以扩展到诸如国家延迟等实际问题,而不需要基于Lyapunov的新分析.
- 这项工作在控制复杂的非线性低值机械系统方面取得了重大进展.
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