Research Initiation Award: Adaptive Quantification of Model Uncertainties

在鲁棒控制和自适应控制中需要的一个关键因素是合理的不确定性界限。不充分的不确定性界限描述将导致分析要么过于保守，要么过于乐观。尽管这一事实得到了广泛的认可，但不确定性的量化问题却很少受到关注。这项研究旨在开发一种新的方法来获得真实植物的估计以及频率上限不确定性。一个起点是通过有理逼近对不确定性进行自适应量化，这结合了识别，很好地进行了内插和逼近。初步结果表明，所提出的方法为不确定性提供了现实的界。这鼓励我们相信，有相当大的潜力进行进一步的探索。概述了开展这项研究的预期策略。据信，预期结果的潜在好处和影响是广泛的，包括对理解和处理不确定性的影响，鲁棒控制与自适应控制的结合，故障检测的新方法等。

A key element needed in robust control and adaptive control is a reasonable uncertainty bound. An inadequate uncertainty bound description will cause analysis to be either overly conservative or dangerously optimistic. Despite this widely appreciated fact, the problem of the quantification of uncertainty has received scant attention. The research intends to develop a new methodology for obtaining an estimate of the true plant as well as a frequency upper bound on the uncertainty. A starting point is the adaptive quantification of uncertainties by rational approximation which combines identification, interpolation and approximation nicely. Preliminary results show that the methods proposed provide realistic bounds for the uncertainty. This encourages us to believe that there is considerable potential for further exploration. The expected strategies for conducting this research are outlined. The potential benefits and the implications of the expected results are believed to be wide-ranging and include new insights into the understanding and treatment of the effects of the uncertainty, integration of robust control and adaptive control, new method for fault detection and so on.