Adaptive Cancellation of Time-Varying Disturbances

时变干扰的自适应消除

• 批准号: 0115070
• 负责人: Marc Bodson
• 金额: $ 18万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-15 至 2005-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0115070&HistoricalAwards=false
• 关键词: Adaptive; Cancellation; Time; Varying; Disturbances

The object of this project is to develop control algorithms for the cancellation of periodic disturbances whose frequency content varies significantly over time. Typical applications include active noise and vibration control, although the problems under consideration are generic and the algorithms are applicable in many other contexts. Two classes of algorithms are considered. They have been studied before for restricted cases and have shown promising results. The algorithms are particularly valuable because of their simplicity and because of the tools available to design them for specific performance goals. In the project, the algorithms will be developed so that they will become applicable to the most general problems with multivariable system and with disturbances having multiple harmonic components. An important intermediate goal will be the analysis of the robustness properties of adaptive algorithms using little known but powerful results of linear time-invariant system equivalence. These results will enable a precise quantification of the robustness margins of the algorithms, and may lead to new strategies for robust design. Another goal is the development of new algorithms for the estimation and tracking of the parameters of periodic signals using multiple sensors. The algorithms will be tested in active noise control or active vibration control experiments.

本项目的目标是开发控制算法，用于消除频率随时间显著变化的周期性干扰。 典型的应用包括主动噪声和振动控制，尽管所考虑的问题是通用的，并且算法适用于许多其他环境。 两类算法被认为是。 他们以前已经研究了有限的情况下，并已显示出有希望的结果。 算法是特别有价值的，因为它们的简单性，因为工具可用于设计它们的特定性能目标。 在该项目中，算法将被开发，使他们将成为适用于最一般的问题与多变量系统和干扰具有多个谐波分量。 一个重要的中间目标将是使用鲜为人知的，但强大的线性时不变系统等价的结果的自适应算法的鲁棒性分析。 这些结果将使一个精确的量化的算法的鲁棒性利润，并可能导致新的策略，强大的设计。 另一个目标是开发新的算法，用于使用多个传感器来估计和跟踪周期信号的参数。 该算法将在主动噪声控制或主动振动控制实验中进行测试。