有效的RBFNN适应控制非线性系统与无与伦比的不确定性.
概括
本研究介绍了一种新型的适应性跟踪控制器,用于非线性系统的辐射基函数神经网络 (RBFNNs). 该方法通过反复改进RBFNNs来确保稳定性和提高性能,即使存在不确定性.
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:
- 非线性植物往往表现出无与伦比的不确定性,对传统的控制方法构成挑战.
- 射线基函数神经网络 (RBFNNs) 提供强大的近似能力,但需要仔细设计以实现适应性控制中的稳定性.
研究的目的:
- 为具有无与伦比的不确定性的非线性植物提出一个有效的RBFNN适应性控制策略.
- 为了确保闭环稳定性和可靠的近似精度,通过将 RBFNN 参数保持在紧的集合中.
主要方法:
- 一种新的代设计方法与后退方法相结合.
- 使用辅助变量和重新设计的紧集,以无限期确保RBFNN有效性.
- 为稳定性分析制定了一个闭环系统模型.
主要成果:
- 提出的代方法将RBFNNs的有效性延长到无限时间.
- 提供了严格的稳定性证明和实际实施指南.
- 一项新的发现表明,过度大的RBFNNs可以降低具有无与伦比的不确定性系统的性能.
结论:
- 开发的自适应跟踪控制器确保了闭环稳定性,并提高了非线性系统的性能.
- 代设计方法为RBFNN自适应控制提供了一个强大的方法,解决了以前方法的局限性.
- 该研究提供了关于RBFNNs在复杂控制场景中的实际设计和局限性的宝贵见解.
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