Development Forum 4
Stochastic Modeling, Control and Decision Chair: Feiqi Deng (South China University of Technology, China)
Zidong Wang (Brunel University London, U.K.) |
Panelists: Zengjing Chen (Shandong University)
Xianping Guo (Sun Yat-sen University)
Lixian Zhang (Harbin Institute of Technology)
Quanxin Zhu (Hunan Normal University)
Qinyuan Liu (Tongji University)
Abstract: The control theory of stochastic systems has attracted a lot of attentions from researchers in the areas of mathematics and engineering, and a plenty of developments around the analysis and control have been achieved, yet there are many challenges even for the fundamental issues around this kind of models, for example those on the modeling of the stochastic networked control systems, the new types of control strategies, the random optimization and random decision making. In this forum, five experts have been invited to give their talks on the topics of stochastic modeling, control and decision with intention to share their latest research progress with the interested audience.
Speaker: Zengjing Chen, Shandong University, China
Title: Strategy-Driven Limit Theorems Associated Bandit Problems
Abstract: Motivated by the study of asymptotic behavior of the bandit problems, we obtain several strategy-driven limit theorems including the law of large numbers, the large deviation principle, and the central limit theorem. Different from the classical limit theorems, we develop sampling strategy-driven limit theorems that generate the maximum or minimum average reward. The law of large numbers identifies all possible limits that are achievable under various strategies. The large deviation principle provides the maximum decay probabilities for deviations from the limiting domain. To describe the fluctuations around averages, we obtain strategy-driven central limit theorems under optimal strategies. The limits in these theorems are identified explicitly, and depend heavily on the structure of the events or the integrating functions and strategies. This demonstrates the key signature of the learning structure. Our results can be used to estimate the maximal (minimal) rewards, and to identify the conditions of avoiding the Parrondo’s paradox in the two-armed bandit problem. It also lays the theoretical foundation for statistical inference in determining the arm that offers the higher mean reward.
陈增敬，山东大学教授。曾获国家杰出青年基金、孙冶方经济科学奖、国家自然科学二等奖 （独立）和全国 "五一" 奖章。现任山东大学金融研究院院长、山东国家应用数学中心常务副主任：教育部统计学教学指导委员会副主任委员，主要研究方向：金融数学、计量经济学、概率统计、倒向随机微分方程。
Speaker: Xianping Guo, Sun Yat-sen University, China
Title: Finite horizon semi-Markov games under the probability criterion
Abstract: This talk is on a two-person zero-sum game for finite horizon semi-Markov processes, where the concerned criterion is the probability that the total payoff produced by a system during a finite horizon exceeds a prescribed goal. which can be regarded as the security probability for player 1 as well as the risk probability for player 2. First, we give the characterization of the probability, and establish the Shapley equation and the existence of a saddle point under a suitable condition. Then, we develop a value iterative algorithm to compute an epsilon-saddle point and the value of the game by solving a series of matrix games. Finally, we demonstrate the application of our main results by an example on an inventory system.
郭先平，男，博士，博士生导师，国家杰出青年科学基金获得者(2009年)，1996年于中南大学获博士学位，2002于中山大学晋升为教授，2017年获教育部自然科学二等奖，2018年当选为全国概率统计学会副理事长. 研究兴趣为马氏决策过程、随机博弈、风险控制、等。
Speaker: Lixian Zhang, Harbin Institute of Technology, China
Title: 多模态飞行器随机切换系统建模与控制
Abstract: 本报告所讨论的多模态飞行器是指一类飞行器平台自身结构或所处飞行环境参数呈现变化，导致系统多模态的飞行器。报告首先介绍子母式飞行器、空地两用飞行器、可变形飞行器、跟踪间歇遮挡目标的飞行器等若干多模态飞行器案例，随后着重汇报目标随机间歇遮挡下旋翼无人机跟踪问题的随机切换系统建模及控制方法，并给出随机切换系统方法在多模态飞行器控制应用的展望。
张立宪，哈尔滨工业大学航天学院教授、博士生导师，国家杰出青年科学基金获得者，入选国家高层次人才计划，IEEE Fellow，IET Fellow，连续8年全球高被引学者。现为哈工大航天学院副院长、人工智能研究院副院长，国家某部委某领域重大优先方向专家组成员。在切换系统、优化决策与控制、特种机器人控制等方向上承担基金委、科工局、军科委、航天科技/科工集团等课题。获国家自然科学二等奖，黑龙江省自然科学奖一等奖。
Speaker: Quanxin Zhu, Hunan Normal University, China
Title: Existence-Uniqueness and Stabilization of Solutions for a Class of Highly Nonlinear Neutral Stochastic Nonlinear Delay Systems
Abstract: In this report, we are interested in a class of highly nonlinear neutral stochastic nonlinear delay systems, which does not need to satisfy the linear growth condition. Under the local Lipschtiz condition and the new polynomial growth condition, we establish the existence and uniqueness as well as boundedness of global solution for stochastic highly nonlinear delay systems with neutral-term. Furthermore, we study the stabilization problem of the considered system by using a discrete-time feedback control method. Different from the previous literature, our discrete-time feedback control depends on Markov switching signals with a delay. Finally, an illustrative example is given to check the theory results.
朱全新，博士，2级教授，湖南师范大学潇湘学者特聘教授, 博士生导师, 湖南省科技创新领军人才、湖南省芙蓉学者特聘教授、德国洪堡基金高级研究学者，计算与随机数学教育部重点实验室副主任，复杂系统的控制与优化湖南省高校重点实验室主任，IEEE高级会员、中国自动化学会高级会员。主要从事马氏过程、随机非线性系统的稳定与控制理论及应用研究工作, 取得了系列重要进展, 在控制领域国际权威杂志Automatica、IEEE Transactions on Automatic Control等刊物发表SCI收录论文200余篇。获湖南省自然科学奖一等奖（第一完成人）、江苏省高校自然科学奖一等奖（第一完成人）、2018～2022连续五年全球高被引学者、2020-2022连续3年全球前2%顶尖科学家榜单、2014～2022连续九年爱思唯尔中国高被引学者榜单等。主持国家自然科学基金项目5项，省部级项目10 项，作为第二参与人承担国家自然科学基金重点项目1项。担任中国工程概率统计学会常务理事、中国数学会概率统计分会理事、中国工业与应用数学学会系统与控制数学专业委员会委员、中国自动化学会自适应动态规划与强化学习专业委员会委员、中国TCCT随机系统与控制学组委员、国际权威杂志IEEE Transactions on Automation Science and Engineering等6个国际SCI刊物的副主编或编委。
Speaker: Qinyuan Liu, Tongji University, China
Title: 基于双比特率周期编码的分布式卡尔曼滤波
Abstract: 本报告从概念、应用和挑战等角度介绍了分布式卡尔曼滤波相关知识，着重针对网络传输能力有限的分布式系统，探讨了双比特率周期编码信息传输方式，给出了相应的分布式卡尔曼滤波器的设计方法，并进一步讨论所提出估计算法的性能分析问题。
刘钦源，同济大学电子与信息工程学院教授、博士生导师。2017年6月获清华大学工学博士学位。研究方向包括分布式感知、多传感器数据融合、异常检测和故障诊断、大数据分析和机器学习等。出版英文专著1部，发表IEEE TAC及Automatica论文10余篇。主持国家自然科学基金优秀青年科学基金、上海市晨光学者等项目。曾获吴文俊人工智能科学技术优秀青年奖、ACM上海新星奖、世界人工智能大会青年优秀论文提名奖、中国自动化学会优秀博士学位论文奖等荣誉。
