具有预定义的缓解比率的自适应性神经泽塔倒退. 适用于直流电机的应用
IEEE transactions on cybernetics
|March 4, 2025
概括
这项研究为不确定的非线性系统引入了自适应性神经齐塔后退控制. 该方法通过预先确定的缓冲比率实现实际稳定,使用神经网络补偿未知的非线性.
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.
- 人工智能的人工智能
背景情况:
- 不确定的非线性系统带来了重要的控制挑战.
- 通过预定义的缓冲比率实现实际稳定对于系统性能至关重要.
- 通常需要对未知的非线性进行在线补偿.
研究的目的:
- 为不确定的非线性系统开发一种自适应的神经泽塔回退控制策略.
- 为了使预先确定的缓冲比率实现实际稳定.
- 为了补偿在线未知的非线性.
主要方法:
- 适应性神经泽塔后退控制.
- 神经网络 (NN) 与梯度下降训练的整合,用于在线非线性补偿.
- 使用动态命令波器来构建NN梯度.
- 应用二级利亚普诺夫稳定性标准.
主要成果:
- 拟议的控制策略确保了闭环系统的实际稳定.
- 根据特定的参数选择规则,可以实现预先确定的缓冲比.
- 神经网络有效地弥补了在线未知的非线性.
- 在扰乱直流 (DC) 电机系统上的实验验证证证了该方法的优势.
结论:
- 适应性神经齐塔倒退控制策略为不确定的非线性系统提供了有效的稳定.
- 这种方法可以精确控制系统的阻尼比率.
- 使用神经网络和动态过器提高了对系统不确定性的稳定性.
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