基于近似的适应性固定时间跟踪控制,用于不确定的高阶非线性系统,这些系统的参数在时间上变化,输入非线性未知
Xiyu Zhang1,2, Zhi Yang1, Youjun Zhou3
1School of Mathematics and Computer Science, Guangxi Science and Technology Normal University, Laibin, 546199, China.
本研究介绍了一种基于自适应神经网络 (NN) 的固定时间跟踪控制 (FTTC) 方案,用于不确定的非线性系统. 该方法确保追踪错误在固定的时间内趋同,尽管存在未知的参数和非线性.
科学领域:
- 控制系统工程 控制系统工程
- 非线性动力学是一种非线性动力学.
- 人工智能在控制中
背景情况:
- 高阶非线性系统经常表现出复杂的动态和不确定性.
- 现有的固定时间跟踪控制 (FTTC) 方法在时间变化的参数和未知的输入非线性方面扎.
研究的目的:
- 为不确定的高阶非线性系统开发基于自适应神经网络 (NN) 的FTTC方案.
- 解决因时间变化的参数和未知的输入非线性而带来的挑战.
- 为了实现追踪错误的固定时间趋同.
主要方法:
- 利用神经网络 (NN) 对于系统建模的近似.
- 应用自适应控制和固定时间控制理论.
- 内置的后退控制和纳斯姆增强函数 (NGF) 技术.
- 开发了用于参数估计的自适应控制规律.
主要成果:
- 拟议的FTTC方案保证在固定的时间内将跟踪错误趋同到零的小邻里.
- 闭环系统内的所有信号都保持在边界内.
- 该策略有效地处理由于输入非线性而导致的未知控制收益.
结论:
- 基于NN的自适应FTTC策略为不确定的非线性系统提供了强大的解决方案.
- 该方法在具有挑战性的条件下证明了在实现精确跟踪方面的有效性.
- 模拟结果验证了拟议的控制方案的理论性能.
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