报告题目：Direct Data-driven Analysis and Design of Linear Systems（线性系统的直接数据驱动分析与设计）
报告人：游科友 教授 清华大学
报告时间：2024年12月20日（周五）14:50-15:50
主持人：罗月梅 副教授 南京信息工程大学
报告人简介：
游科友博士，清华大学自动化系长聘教授，分别于2007年在中山大学数计学院获得学士学位和2012年在新加坡南洋理工大学电气与电子工程学院获得博士学位。在南洋理工大学短暂从事博士后研究工作之后，于2012年就职于清华大学至今，在意大利都灵理工大学、香港科技大学、墨尔本大学等地任访问学者。其研究工作主要集中在控制、优化与学习的交叉领域及其在自主系统中的应用。于2010年获得第29届中国控制会议关肇直最佳论文奖，2019年获得亚洲控制学会Temasek青年教育者奖，并于2017年和2023年分别获得国家优秀青年基金和杰出青年基金项目。担任Automatica、IEEE Transactions on Control of Network Systems和IEEE Transactions on Cybernetics等期刊编委。
报告摘要：Modern control theory has been firmly rooted in the state-space model, and then adopts system identification (SysId) followed by model-based control design methods. In this talk, we are motivated by two questions that possibly promote rethinking of this foundation: (a) whether SysId is indispensable to control design, and (b) if not, can we address it in a direct data-driven fashion (bypassing the SysId step)? In particular, via a new concept of sufficient richness of input sectional data, we first establish the necessary and sufficient condition for the minimum sample data for property ID (system analysis) of unknown linear systems. Specifically, the input sectional data is sufficiently rich for property ID if and only if it spans a linear subspace that contains a property dependent minimum linear subspace, any vector basis of which can also be easily used to form the minimum excitation input. Interestingly, we show that many structural properties can be identified with the minimum input that is however unable to complete SysId. Then, we propose an optimal data-enabled LQR formulation in the sense of achieving minimum regret of the quadratic cost, and design a novel data-enabled policy optimization (DeePO) method using only a batch of online persistently exciting (PE) data. Finally, we numerically validate the theoretical results and demonstrate the computational and sample efficiency of our method.
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未来技术学院
2024年12月18日