切换基于ETM的神经自适应输出反控制非关联性随机MIMO非线性系统的延迟约束
Xiaona Song1, Peng Sun1, Choon Ki Ahn2
1School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
概括
这项研究引入了一个新的神经适应控制方法,用于复杂的非线性系统. 该方法确保了准确的控制性能,同时避免了约束违规,提高了系统的稳定性和可靠性.
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:
- 非类随机多输入,多输出 (MIMO) 非线性工厂由于其复杂的动态和不可测量的状态,存在重大控制挑战.
- 现有的控制方法经常与计算复杂性和过引发的错误在这些系统中扎.
研究的目的:
- 开发一种新型的神经自适应输出反控制策略,用于非亲属的随机MIMO非线性植物.
- 在控制设计中解决状态估计,计算负担和过器错误效应的问题.
主要方法:
- 使用辐射基函数神经网络进行状态估计的K过器状态观察者的设计.
- 建立一个自适应的命令过后退输出反控制框架,其中包含一个改进的命令过器和一个小数顺序参数.
- 引入修改后的错误补偿信号和延迟约束概念,以减轻过器错误.
- 实施修订的切换事件触发机制以优化网络资源,控制冲动和准确性.
主要成果:
- 拟议的控制方法保证了跟踪错误在特定时间内汇聚到用户定义的区域.
- 该方法有效地防止违反推迟输出约束的情况.
- 理论分析证实了开发的控制策略的稳定性和性能.
结论:
- 新型神经自适应输出反控制方法为非亲属的随机MIMO非线性植物提供了强大的和高效的解决方案.
- 集成先进技术,如分数顺序过器和事件触发机制,可以提高控制精度和资源管理.
- 该研究通过说明性示例验证了有效性,证明了对现有方法的优越性.
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