【云端学术 同心战“疫”】信息科学与工程学院线上学术讲座专场

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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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工业数字化技术和企业效益提升都离不开对数据的处理和应用。工业大数据和日常的大数据有什么不同？大数据算法、机器学习、人工智能对于我们的制造业是必须的或是个可选项？本报告将结合我们身边的例子，从知识自动化的角度，阐述工业大数据的基本概念和方法，讨论大数据在工业中的作用和发展方向。

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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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主要研究方向为机器人学、人工智能、控制理论等。曾主持或参与国家863项目、国家973项目、国家自然科学基金等30余项。近十年来发表论文200余篇，其中SCI收录80余篇。发表学术专著、译著以及主编教材等10余部，获得国内授权发明专利20余项。曾获得国家级教学成果奖、教育部自然科学奖、北京市科技进步奖、军队科技进步奖、中国自动化学会自然科学奖和中国电子学会电子信息科学技术奖等。