非线性系统的动态表面智能稳健控制与固定时间滑动模式观察员
IEEE transactions on cybernetics
|September 17, 2024
概括
本研究引入了一种针对非线性系统的新型强有力的控制策略,提高了跟踪精度和融合速度. 该方法有效地处理复杂的非线性和干扰,以提高系统性能.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 非线性动力学是一种非线性动力学.
背景情况:
- 非线性系统在实现高跟踪控制精度和快速有限时间融合方面存在重大挑战.
- 复杂的非线性和未知的干扰阻碍了可靠的控制性能.
研究的目的:
- 为改进有限时间跟踪控制提出一个具有固定时间滑动模式观测器 (DSIRC-SMO) 的动态表面智能稳健控制策略.
- 提高非线性系统的近似精度和反干扰能力.
主要方法:
- 设计了一个基于预测器的自适应模糊神经网络 (P-AFNN),以模仿复杂的非线性,使用预测错误进行重量适应.
- 将固定时间滑动模式观察器 (SMO) 集成到动态表面控制中,以解决干扰和建模错误.
- 证明了SMO的固定时间收和DSIRC-SMO整体战略的有限时间收.
主要成果:
- P-AFNN在近似非线性系统动态方面表现出更好的准确性.
- 集成的SMO通过及时更新动态表面信息来增强系统的反干扰能力.
- 证实了DSIRC-SMO战略的有效实施,并保证了有限时间的融合.
结论:
- 拟议的DSIRC-SMO战略有效地提高了非线性系统的跟踪控制精度和有限时间融合.
- 结合P-AFNN和固定时间SMO,为复杂的非线性控制问题提供了强大的解决方案.
- 通过数值和废水处理过程模拟进行验证,证明了实际适用性.
相关概念视频
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01:21Linear Approximation in Time Domain
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01:23Linear time-invariant Systems
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01:24One-Degree-of-Freedom System
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