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Springer 新近出版中国学者刘淑君随机控制方面著作

已有 7257 次阅读 2012-9-10 16:07 |个人分类:科研与同行|系统分类:科研笔记| Springer, 中国学者, 出版著作


Springer 出版一本新书 (Forthcoming July 31 2012)

Stochastic Averaging and Stochastic Extremum Seeking

作者是东南大学数学系的刘淑君和UC San Diego 的Krstic. 



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. 
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.


Krstic 的主页 以上文章可在其主页下载。





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.


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