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A Mean-Field Game for a Forward-Backward Stochastic System With partial observation and Common Noise

IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 746-759页

作者： Pengyan Huang Guangchen Wang Shujun Wang Hua Xiao the School of Control Science and Engineering Shandong University the School of Statistics and Mathematics Shandong University of Finance and Economics IEEE the School of Management Shandong University the School of Mathematics and Statistics Shandong University

This paper considers a linear-quadratic(LQ) meanfield game governed by a forward-backward stochastic system with partial observation and common noise,where a coupling structure enters state equations,cost functionals and observation equations.Firstly,to reduce the complexity of solving the meanfield game,a limiting control problem is introduced.By virtue of the decomposition approach,an admissible control set is proposed.Applying a filter technique and dimensional-expansion technique,a decentralized control strategy and a consistency condition system are derived,and the related solvability is also addressed.Secondly,we discuss an approximate Nash equilibrium property of the decentralized control strategy.Finally,we work out a financial problem with some numerical simulations.

关键词： Decentralized control strategy ϵ-Nash equilibrium forward-backward stochastic system mean-field game partial observation

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Data-Driven Modeling of partially Observed Biological Systems

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Communications on Applied Mathematics and Computation 2024年第1期6卷 739-754页

作者： Wei-Hung Su Ching-Shan Chou Dongbin Xiu Department of Mathematics The Ohio State UniversityColumbusOH43221USA

We present a numerical approach for modeling unknown dynamical systems using partially observed data,with a focus on biological systems with(relatively)complex dynamical behavior.As an extension of the recently developed deep neural network(DNN)learning methods,our approach is particularly suitable for practical situations when(i)measurement data are available for only a subset of the state variables,and(ii)the system parameters cannot be observed or measured at all.We demonstrate that,with a properly designed DNN structure with memory terms,effective DNN models can be learned from such partially observed data containing hidden parameters.The learned DNN model serves as an accurate predictive tool for system analysis.Through a few representative biological problems,we demonstrate that such DNN models can capture qualitative dynamical behavior changes in the system,such as bifurcations,even when the parameters controlling such behavior changes are completely unknown throughout not only the model learning process but also the system prediction process.The learned DNN model effectively creates a“closed”model involving only the observables when such a closed-form model does not exist mathematically.

关键词： Deep neural network(DNN) Governing equation discovery Biological system partial observation

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Localization-Based Distributed Control for Large Discrete-Event Systems under partial observation

Localization-Based Distributed Control for Large Discrete-Ev...

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第30届中国控制与决策会议

作者： Renyuan Zhang Kai Cai School of Automation Northwestern Polytechnical University Department of Electrical and Information Engineering Osaka City University

Recently we developed partial-observation supervisor localization, a top-down approach to distributed control of discrete-event systems（DES） under partial observation. Its essence is the decomposition of the partial-observation monolithic supervisor into partial-observation local controllers for individual controllable events. In this paper we extend the partial-observation supervisor localization to large-scale DES, for which the monolithic supervisor may be incomputable. Specifically, we first employ an efficient heterarchical supervisor synthesis procedure to compute a heterarchical array of partial-observation decentralized supervisors and partial-observation coordinators. Then we localize each of these supervisors/coordinators into partial-observation local controllers. This procedure suggests a systematic approach to the distributed control of large-scale DES under partial observation. The results are illustrated by a system of automatic guided vehicles（AGV） serving a manufacturing workcell.

关键词： Discrete-event systems supervisory control supervisor localization partial observation automata

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Data-driven Inverter-based Volt/VAr Control for partially Observable Distribution Networks

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CSEE Journal of Power and Energy Systems 2023年第2期9卷 548-560页

作者： Tong Xu Wenchuan Wu Yiwen Hong Junjie Yu Fazhong Zhang Southwest Electric Power Design Institute Co. Ltd.of China Power Engineering Consulting GroupChengduSichuan 610021China Tsinghua University Beijing 100084China Department of Electrical Engineering State Key Laboratory of Power SystemsTsinghua UniversityBeijing 100084China Guangdong Power Grid Zhongshan Power Supply Bureau Zhongshan 528400China

For active distribution networks(ADNs)integrated with massive inverter-based energy resources,it is impractical to maintain the accurate model and deploy measurements at all nodes due to the large-scale of ADNs.Thus,current models of ADNs usually involve significant errors or even unknown occurances.Moreover,ADNs are usually partially observable since only a few measurements are available at pilot nodes or nodes with significant users.To provide a practical Volt/Var control(VVC)strategy for such networks,a data-driven VVC method is proposed in this paper.First,the system response policy,approximating the relationship between the control variables and states of monitoring nodes,is estimated by a recursive regression closed-form solution.Then,based on real-time measurements and the newly updated system response policy,a VVC strategy with convergence guarantee is realized.Since the recursive regression solution is embedded in the control stage,a data-driven closedloop VVC framework is established.The effectiveness of the proposed method is validated in an unbalanced distribution system considering nonlinear loads,where not only the rapid and self-adaptive voltage regulation is realized,but also systemwide optimization is achieved.

关键词： Data-driven distribution networks partial observation Volt/VAr control

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Effect of observation time on source identification of diffusion in complex networks

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Chinese Physics B 2022年第7期31卷 97-103页

作者：史朝义张琦楚天广 College of Engineering Peking UniversityBeijing 100871China School of Information Technology and Management University of International Business and EconomicsBeijing 100105China

This paper examines the effect of the observation time on source identification of a discrete-time susceptible-infectedrecovered diffusion process in a network with snapshot of partial nodes.We formulate the source identification problem as a maximum likelihood(ML)estimator and develop a statistical inference method based on Monte Carlo simulation(MCS)to estimate the source location and the initial time of diffusion.Experimental results in synthetic networks and real-world networks demonstrate evident impact of the observation time as well as the fraction of the observers on the concerned problem.

关键词： complex network source identification statistical inference partial observation

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一类部分观察随机系统的最优控制

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厦门大学学报(自然科学版) 1987年第6期 651-658页

作者：林成德厦门大学计算机与系统科学系

对于由观察和控制二者的联合概率分布所组成的充许控制集,本文证明了:在关于状态方程的某些正则性,以及其他的一些凸性假设下,当观察系数与其一阶偏导数均为本性有界时,最优控制仍存在。

关键词： Optimal control partial observation Separation problem Stochastio system Zakai equation

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Maximum Principle for partial Observed Zero-Sum Stochastic Differential Game of Mean-Field SDEs

Maximum Principle for Partial Observed Zero-Sum Stochastic D...

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第36届中国控制会议

作者： Maoning Tang Qingxin Meng Department of Mathematical Sciences Huzhou University

ISBN: (纸本)9781538629185

In this paper, we consider a partial observed two-person zero-sum stochastic differential game problem where the system is governed by a stochastic differential equation of mean-field type. Under standard assumptions on the coefficients, the maximum principles for optimal open-loop control in a strong sense as well as a weak one are established by the associated optimal control theory in Tang and Meng（2016）.

关键词： Zero-Sum Stochastic Differential Game Stochastic Maximum Principle partial observation Open-Loop Saddle Mean-Field

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