Advances in control theory

Researchers in the Advanced Systems Analysis (ASA) Program make advances in contemporary control theory related to control under incomplete information, control of distributed systems, and construction of the attainability domains.

The problem of guiding a system onto a terminal set from an unknown initial point was explored in an ASA study [1][2], where the authors developed an approach for linear control systems to construct a closed-loop control based on an open-loop treatment. For linear systems with discontinuous input matrices, ASA researchers carried out an asymptotic analysis enabling the construction of an attainability domain given that phase constraints can be fuzzy, reflecting the possibility of shock scenarios and a limited precision of real-life data respectively [3][4].

In addition, ASA work in this field used a probabilistic framework for modeling diffusion in networks to demonstrate how network diffusion can be externally manipulated by injecting time-varying input functions at individual nodes and by modifying the dominant diffusion modes [5].

References

[1] Strelkovskii NV (2015). Constructing a strategy for the guaranteed positioning guidance of a linear controlled system with incomplete data. Moscow University Computational Mathematics and Cybernetics. 39(3):126-134.

[2] Kryazhimskii AV & Strelkovskii NV (2015). An Open-loop criterion for the solvability of a closed-loop guidance problem with incomplete information: Linear control systems. Proceedings of the Steklov Institute of Mathematics. 291(S1):113-127.

[3] Chentsov AG & Baklanov AP (2015). On the question of construction of an attraction set under constraints of asymptotic nature. Proceedings of the Steklov Institute of Mathematics 291(S1):40-55.

[4] Chentsov AG & Baklanov A (2015). On an asymptotic analysis problem related to the construction of an attainability domain. Proceedings of the Steklov Institute of Mathematics 291:279-298.

[5] Chan WHR, Wildemeersch M, Quek TQS (2015): Diffusion control in multi-agent networks, in: 54th Annual Conference on Decision and Control (CDC). 15-18 December 2015, Osaka, Japan. IEEE, pp. 4190-4195. ISBN 978-1-4799-7884-7

Collaborators

Lomonosov Moscow State University, Russia

Institute of Mathematics and Mechanics, Ural Branch of Russian Academy of Sciences, Russia

Stanford University, California, USA

Singapore University of Technology and Design, Singapore

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Last edited: 16 March 2016