适应性神经基SMC用于异常扰乱的系统,在非周期性采样下具有死区.
IEEE transactions on cybernetics
|April 8, 2025
概括
本研究介绍了适应性神经网络 (NN) 滑动模式控制 (SMC) 对于有不规则采样和输入死区的系统. 该方法确保了系统的稳定性和性能,尽管这些具有挑战性的条件.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能在控制中
- 非线性系统理论 非线性系统理论
背景情况:
- 采样数据系统经常面临非周期性采样间隔的挑战,这在控制设计中引入了复杂性.
- 输入截止区的非线性大大降低了系统性能和稳定性.
- 由于其快速和缓慢的动态,奇异扰动系统需要专门的控制策略.
研究的目的:
- 开发一种基于自适应神经网络 (NN) 的滑动模式控制 (SMC) 策略,用于采样数据单独扰乱的系统.
- 为应对非周期性采样间隔和输入截止区非线性所带来的挑战.
- 为了保证系统稳定性和性能在指定的挑战条件下.
主要方法:
- 使用非均逗留概率方法来建模不规则的抽样间隔.
- 适应神经网络 (NN) 方案用于在线估计和补偿输入期区非线性.
- 一个新的滑动模式控制器旨在处理采样模式和单一扰动参数的变化.
主要成果:
- 拟议的适应性NN-SMC战略确保了系统状态的指数级终极局限性.
- 控制策略保证了闭环系统中预定义的滑动表面的可达性.
- 建议的控制方法的有效性通过实践示例来验证.
结论:
- 开发的自适应性NN-SMC有效地处理异常扰动系统中的非周期性采样和输入死区.
- 控制方法提高了系统的稳定性和性能,确保了稳定性和状态的融合.
- 拟议的方法为采样数据系统中复杂的控制问题提供了可行的解决方案.
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