博文
Springer 新近出版中国学者刘淑君随机控制方面著作
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重发今年6月份的博文，介绍同行刘淑君博士的工作。
https://wap.sciencenet.cn/blog-669170-611195.html
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Springer 出版一本新书 (Forthcoming July 31 2012)：
1. S.-J.Liu and M.Krstic (2011). Stochastic Nash equilibrium seeking for games with general nonlinear payoffs. SIAM Journal on Control and Optimization, vol. 49., no.4, 1659-1679.
Stochastic Averaging and Stochastic Extremum Seeking
（2008.7-2009.7)。他们主要发表的论文
作者是东南大学数学系的刘淑君和UC San Diego 的Krstic.
博主猜测书的内容主要来自第一作者在UCS博士后期间的工作
2. Liu, S. J., & Krstić, M. (2010b). Stochastic averaging in continuous time and its applications to extremum seeking. IEEE Transactions on Automatic Control, 55(10), 2235–2250.
3. Liu, S. J., & Krstić, M. (2010a). Continuous-time stochastic averaging on the infinite interval for locally Lipschitz systems. SIAM Journal on Control and Optimization, 48(5), 3589–3622.
4. S. -J. Liu and M. Krstic. Stochastic source seeking for nonholonomic unicycle. Automatica, 2010, 46(9), 1443-1453.
第一作者作为负责人已经得到了国家自然科学基金的两项资助（青年和面上），其个人主页
Krstic 的主页：http://flyingv.ucsd.edu/ 以上文章可在其主页下载。
第一作者的博士导师是科学院系统所张纪峰研究员，硕士导师是四川大学数学系曹广福教授（科学网博主）
博士论文：随机非线性系统的控制器设计和闭环性能分析，2007。
硕士论文：多重交换算子的谱和数值域，2002。
值得赞叹的是：第一作者似乎在一年的期间完成了上述工作，后三篇均为2009年7月前投稿。目前的工作引人关注。
书的内容如下：
Stochastic Averaging and Stochastic Extremum Seeking develops methods of mathematical analysis inspired by the interest in reverse engineering and analysis of bacterial convergence by chemotaxis and to apply similar stochastic optimization techniques in other environments.
The first half of the text presents significant advances in stochastic averaging theory, necessitated by the fact that existing theorems are restricted to systems with linear growth, globally exponentially stable average models, vanishing stochastic perturbations, and prevent analysis over infinite time horizon.
The second half of the text introduces stochastic extremum seeking algorithms for model-free optimization of systems in real time using stochastic perturbations for estimation of their gradients. Both gradient- and Newton-based algorithms are presented, offering the user the choice between the simplicity of implementation (gradient) and the ability to achieve a known, arbitrary convergence rate (Newton).
The design of algorithms for non-cooperative/adversarial games is described. The analysis of their convergence to Nash equilibria is provided. The algorithms are illustrated on models of economic competition and on problems of the deployment of teams of robotic vehicles.
Bacterial locomotion, such as chemotaxis in E. coli, is explored with the aim of identifying two simple feedback laws for climbing nutrient gradients. Stochastic extremum seeking is shown to be a biologically plausible interpretation for chemotaxis. For the same chemotaxis-inspired stochastic feedback laws, the book also provides a detailed analysis of convergence for models of nonholonomic robotic vehicles operating in GPS-denied environments.
The book contains block diagrams and several simulation examples, including examples arising from bacterial locomotion, multi-agent robotic systems, and economic market models.
Stochastic Averaging and Extremum Seeking will be informative for control engineers from backgrounds in electrical, mechanical, chemical and aerospace engineering and to applied mathematicians. Economics researchers, biologists, biophysicists and roboticists will find the applications examples instructive.
详细见网页：webpage
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