2020-01-09 11:01   自動化學院 審核人:   (點擊: )

報告題目:Learning-based Optimal Control with Guaranteed Stability

報告人:姜鐘平教授 (紐約大學)




報告簡介:This talk considers learning-based control design for continuous-time dynamical systems with unknown dynamics. It introduces a new design paradigm, called “Robust Adaptive Dynamic Programming (RADP)”, that is fundamentally different from traditional control theory. In the classical paradigm, controllers are often designed for a given class of dynamical control systems; it is a model-based design. In the RADP paradigm, controllers are learned online using real-time input-output data collected along the trajectories of the control system in question. An entanglement of techniques from reinforcement learning and model-based control theory is advocated to find a sequence of suboptimal controllers that will approximate the optimal solution as learning steps increase. On the one hand, this RADP paradigm overcomes the well-known “curse of dimensionality” and the “curse of modeling” associated with Bellman's Dynamic Programming. On the other hand, rigorous stability and robustness analysis can be derived for the closed-loop system with real-time learning-based controllers. The effectiveness of RADP as a new framework for data-driven nonlinear control design is demonstrated via its applications to power systems, autonomous vehicles, and biological motor control.


姜鐘平,自1999年受聘于美國紐約科技大學(現合并成為紐約大學工學院)以來,歷任助理教授、副教授和教授。是非線性小增益理論的關鍵創立者,主要研究領域為穩定性理論、魯棒自適應非線性控制及其在通訊網絡、欠驅動力學系統、多智能體和系統神經學中的應用。基于他的研究工作,姜博士于1998年獲得澳大利亞伊麗莎白二世杰出研究獎,2001年獲得美國國家科學基金會成就獎,2005年獲日本科學振興會研究獎, 2007年獲得中國自然科學基金會海外杰出華人研究獎(中科院系統科學研究所)。2008年當選為美國電氣電子工程師協會會士。2009年獲選教育部“長江學者”講座教授(北京大學)。2013年當選為國際自動控制聯合會會士。2017年當選為中國自動化學會會士。2018年入選Clarivate Analytics高被引研究員。

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