【雲端學術 同心戰“疫”】信息科學與工程學院線上學術講座專場

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In this talk, a recently on the teleoperation of sward robotics will be introduced. During swarm teleoperation, the human operator may threaten the distance-dependent inter-robot communications and, with them, the connectivity of the slave swarm. To prevent the harmful component of the human command from disconnecting the swarm network, we develop a constructive strategy to dynamically modulate the interconnections of, and the locally injected damping at, all slave robots. By Lyapunov-based set invariance analysis, the explicit law for updating that control gains has been rigorously proven to synchronize the slave swarm while preserving all interaction links in the tree network. By properly limiting the impact of the user command rather than rejecting it entirely, the proposed control law enables the human operator to guide the motion of the slave swarm to the extent to which it does not endanger the connectivity of the swarm network.

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工業數字化技術和企業效益提升都離不開對數據的處理和應用。工業大數據和日常的大數據有什麽不同？大數據算法、機器學習、人工智能對於我們的製造業是必須的或是個可選項👩‍👧‍👧？本報告將結合我們身邊的例子，從知識自動化的角度📝，闡述工業大數據的基本概念和方法，討論大數據在工業中的作用和發展方向🙆🏿‍♀️🚵🏽‍♂️。

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In this talk, we introduce distributed continuous-time algorithms to solve the optimal resourceallocation problem with time-varying quadratic cost functions for multi-agent systems. The objective is to allocate a quantity of resources while optimizing the sum of all the local time-varying cost functions. Here the optimal solutions are trajectories rather than some fixed points. We consider a large number of agents that are connected through a network, and our algorithms can be implemented using only local information. By making use of the prediction-correction method and the nonsmooth consensus idea, we first design distributed algorithms to deal with the case when the time-varying cost functions have identical Hessians. We further propose estimator-based algorithms relying on distributed average tracking estimators to address the case of nonidentical Hessians. In each case, it is proved that the solutions of the proposed dynamical systems with certain initial conditions asymptotically converge to the optimal trajectories. The effectiveness of the proposed distributed continuous-time optimal resource allocation algorithms are illustrated through simulations.

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主要研究方向為機器人學、人工智能、控製理論等🐤。曾主持或參與國家863項目、國家973項目、國家自然科學基金等30余項。近十年來發表論文200余篇，其中SCI收錄80余篇。發表學術專著💏、譯著以及主編教材等10余部🙅🏻，獲得國內授權發明專利20余項😪。曾獲得國家級教學成果獎、教育部自然科學獎🧑🏿‍🦳、北京市科技進步獎👨🏼‍🌾💡、軍隊科技進步獎、中國自動化學會自然科學獎和中國電子學會電子信息科學技術獎等。