報(bào)告題目：Data-Driven Robust Model Predictive Control
報(bào)告時(shí)間： 2025. 09. 22（周一）上午10：00
報(bào)告地點(diǎn)： 民主樓118
報(bào)告人：鄧?yán)?/span> (博士后)，University of Alberta
摘要: Model predictive control (MPC) has become an advanced control approach due to its capability of receding horizon control and handling physical constraints. Typically, a prediction model is required to be accurate enough to capture the true dynamics of a real system, thus ensuring the closed-loop performance. However, with the increase of the complexity of controlled systems in engineering, deriving accurate models is becoming more and more challenging, and even impossible. To address this challenge, this talk introduces two data-driven MPC approaches for unknown systems. First, for linear time-invariant (LTI) systems, we present a data-driven robust MPC method that employs a terminal inequality constraint, eliminating the need for explicit system identification or online estimation. Second, for unknown linear time-varying (LTV) systems where the time-varying system matrices are assumed to lie within an unknown polytope, we propose a robust MPC scheme with event-triggered learning. In this framework, model estimation and polytope learning are activated only when necessary, thereby ensuring both robustness and efficiency.
報(bào)告人簡介：鄧?yán)妫幽么蟀柌髮W(xué)電氣與計(jì)算機(jī)工程系博士后。目前主要從事數(shù)據(jù)驅(qū)動與基于學(xué)習(xí)的魯棒模型預(yù)測控制研究。2023年8月獲阿爾伯塔大學(xué)電氣與計(jì)算機(jī)工程系（控制系統(tǒng)專業(yè)）博士學(xué)位，導(dǎo)師是Tongwen Chen教授（加拿大皇家科學(xué)院院士、加拿大工程院院士、加拿大智能監(jiān)測與控制領(lǐng)域首席科學(xué)家、IEEE Fellow、IFAC Fellow）。在國際重要期刊和會議累計(jì)發(fā)表論文24篇。擔(dān)任《Automatica》、《IEEE Transactions on Automatic Control》等多個(gè)權(quán)威期刊審稿人。曾擔(dān)任American Control Conference等國際會議分會/專題主席。
