Research Initiation Award: System Identification for Control Design Applications

本提案涉及最恶劣环境领域的议题， 动力系统的实例识别 的目标 这项研究计划是为了开发方法， 减少不确定性的系统辨识 围绕着一个给定的动力系统， 与鲁棒控制理论相匹配。 将有三 集中的地区。 第一个研究领域将 线性时不变的BE算法开发 系统使用先验和 实验信息 的误差准则 将选择与以下各项兼容的标识 鲁棒控制设计技术 将进行研究 每一个选择的内在困难 的先验和实验信息， 基于信息的复杂性理论。 第二个研究领域是研究和 开发最坏情况识别方法， 非线性模型线性化的确定 系统. 这项研究将发展研究方法 局部不稳定识别的内在极限 光滑非线性系统的线性化动力学， 实验数据来自闭环 实验 第三个研究领域是 离线自适应控制技术。 这项研究将 研究最坏情况系统辨识的融合 用鲁棒控制理论， 有效和非保守的方法， 减少不确定性的工具， 反馈控制器的后续设计， 达到给定的性能目标。

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.