Control-Oriented System Identification

小行星9509539 建议的研究的目的是开发计算工具，桥梁之间的差距差距经典系统识别和鲁棒控制，并了解系统识别的基本限制，特别是在反馈控制的背景下。 拟议的研究将使控制和识别之间的联系更加透明，并允许用户在识别方法，实验设计和模型验证方面做出明智的决定。 该项目将在连接学习和信息理论识别，一般蒙特卡罗方法，并在随时间变化的系统的自适应调节的影响。 主题包括模型验证的确定性方法，样本数据模型验证，模型验证的统计方法，从输入输出数据生成鲁棒控制兼容的不确定性模型的方法，以及确定由时变和噪声引起的系统识别的基本限制。 ***

9509539 Poolla The objective of proposed research is to develop computational tools that bridge the gap between classical system identification and robust control, and to understand the fundamental limits of system identification, particularly in the context of feedback control. The proposed research will make the connection between control and identification more transparent, and allow users to make intelligent decisions on identification methodologies, experiment designs, and model validation . The project will have implications in connecting learning and information theory to identification, in general Monte Carlo methods, and in adaptive regulation of time-varying systems. The topics include deterministic methods for model validation, sample-data model validation, statistical methods for model validation, methods for generating robust control compatible uncertainty models from input-output data, and determining fundamental limits of system identification imposed by time-variation and noise. ***