Research Initiation Award: System Identification for Control Design Applications

这项建议涉及动力系统最坏情况识别领域的主题。这个研究项目的目标是开发系统辨识的方法，以一种与鲁棒控制理论相匹配的方式来减少围绕给定动态系统的不确定性。这将集中在三个领域。第一个研究领域将是利用先验信息和实验信息相结合的线性时不变系统的算法开发。辨识误差准则的选择将与鲁棒控制设计技术相兼容。将利用基于信息的复杂性理论的技术来研究识别每一个先验选择和实验信息的内在困难。第二个研究领域是研究和发展确定非线性系统线性化模型的最坏情况辨识方法。当实验数据来自于闭环实验时，这项研究将发展研究光滑非线性系统局部不稳定线性化动态辨识的内在界限的方法。第三个研究领域是离线自适应控制技术的研究。这项研究将研究最坏情况系统辨识与鲁棒控制理论的融合，目的是开发有效和非保守的方法来利用工具来减少不确定性和随后的反馈控制器的设计，以满足给定的性能目标。

This proposal addresses topics in the area of the worst case identification of dynamical systems. The goals of this research program are to develop methods for system identification that reduce the uncertainty surrounding a given dynamical system in a way that is matched to robust control theory. There will be three areas of concentration. The first area of research will be algorithm development for linear time invariant systems using a combination of a priori and experimental information. The error criterion of identification will be chosen to be compatible with robust control design techniques. A study will be made of the intrinsic difficulty of identification of each choice of a priori and experimental information using the techniques of Information Based Complexity theory. The second research area is the study and the development of worst case identification methods for the determination of linearized models of nonlinear systems. The research will develop methods for study intrinsic limits of identification of locally unstable linearized dynamics of smooth nonlinear systems when experimental data is obtained from closed loop experiments. The third research area is the study of offline adaptive control techniques. The research will study the merging of worst case system identification with robust control theory with the goal of developing efficient and nonconservative methods for utilizing tools for the reduction of uncertainty and the consequent design of feedback controllers in order to meet a given performance goal.