报告题目1：Reference output tracking problems for Boolean Control Networks

报告人：Elena Valcher，University of Padova

报告时间：2026年8月22日（周六）9:30-12:00

报告地点：20-200

报告摘要：Boolean control networks provide an effective framework for modeling and analyzing systems whose dynamics are governed by logical rules, with applications ranging from systems biology to networked decision processes. This talk addresses the problem of output trajectory tracking for Boolean control networks, considering both finite and periodic reference signals. Conditions under which a desired output evolution can be achieved are discussed, together with extensions that account for arbitrary initial conditions and delayed tracking. The presentation also highlights algorithmic tools supporting the analysis and controller synthesis, illustrating how they can be used to systematically solve the corresponding tracking problems. Overall, the talk provides an overview of recent advances in output tracking for logical dynamical systems and outlines several directions for future research.

报告题目2：Distributed state estimation in the presence of disturbances

报告人：Elena Valcher，University of Padova

报告时间：2026年8月23日（周日）9:30-12:00

报告地点：20-200

报告摘要：Distributed state estimation has become a key enabling technology for large-scale networked systems, where centralized architectures are often impractical because of communication, computational, or reliability constraints. This talk considers the problem of estimating the state of a discrete-time linear system from a network of sensors, each having access only to local measurements and partial input information, in the presence of unknown disturbances affecting the system dynamics. We present a distributed observer architecture based on augmented unknown input observers and discuss necessary and sufficient conditions for its existence. The proposed design relies on tools from decentralized dynamic output feedback, making it possible to exploit well-established synthesis techniques while extending them to the distributed unknown-input estimation framework. The resulting methodology provides a systematic approach to the design of distributed observers capable of asymptotically reconstructing the system state despite the presence of unknown inputs.

报告人简介：Elena Valcher (Ph.D. Degree, 1995) is Professor of Control Theory at the University of Padova since 2005. Her current research interests include data-driven methods, distributed estimation, social networks, Boolean control networks, multi-agent systems and consensus problems.  Author of approximately 100 journal papers, 18 book chapters, 120 conference papers and 3 textbooks. She has been an active volunteer since 1999, serving in various roles for IEEE Control Systems Society, IEEE, IFAC and EUCA.  She was IEEE CSS President in 2015, she is currently EUCA President (2024-2025). She was a member of the IFAC Technical Board (2017-2023) and she is the Chair del 2026 IFAC Major Awards Search Committee. She was the Founding Editor in Chief of the IEEE Control Systems Letters (2017-2024) and co-Editor in Chief of Foundations and Trends in Systems and Control since 2023. She was Program Chair of the 2012 IEEE CDC, and co-General Chair of the 2022 IEEE CDC. She is an IEEE Fellow, an IEEE CSS Distinguished Member and an IFAC Fellow.

邀请人：钟杰