Control Design and Optimization of Complex Networked Systems Control, Design and Optimization of Complex Networked Systems Complex networks arise in a variety of engineered systems and modern infrastructures. A distinguished feature of engineered networks is that they are constructed to perform certain tasks, and there are typical performance measures that are used to steer the network towards optimality with respect to the functions that it is supposed to perform. As a result, functionality can lead to characteristic topologies that can typically be complex. Another feature of engineered networks is that they usually operate at their capacity limits. The ability for a complex engineered network to maintain its robustness and functionality in the presence of attacks and/or failures is an outstanding issue in science and engineering with tremendous implications to society and homeland defense. The aims of the proposed exploratory research are to develop a mathematical theory to understand the critical dynamics of the security and robustness of complex engineered networks and to articulate design principles and effective control strategies for future engineered networks that are efficient, secure, robust, and optimally functional. We will investigate the interplay among the functionality of engineered networks expressed through some cost function, the evolution of large and complex networks under the pressure exerted via the cost function, and the robustness and security of such networks in their growing as well as in the eventual optimized state. We will define the capacity of a network with respect to its function based on laws of physical flows and study the impact of networks evolution toward the capacity limit on its topological structure. We will develop effective methods to control and design engineered complex networked systems to significantly enhance their robustness and security. In this regard, we will specifically address (1) suppression of cascading failures and traffic congestion in complex engineered networks by soft control that preserves all nodes in the network and its connecting topology and (2) the impact of the functionality, i.e., the optimization criteria on the evolutionary development of networks and the vulnerability and/or robustness of the eventual networks. Results obtained from the exploratory research will form the base for a more comprehensive program of interdisciplinary study on complex networks at Arizona State University (ASU) with significant applications in natural sciences, engineering, social sciences, economy, and defense. The engineering PI (Lai) has expertise in nonlinear dynamical systems and complex networks. The mathematics PI (Armbruster) has extensive experience in nonlinear dynamics and distribution and production networks. They have collaborated previously in the area of chaotic dynamics. Their combined expertise will make the proposed research successful and highly productive. Intellectual Merit and Transformative Potential: The proposed research will focus on fundamental issues concerning the functionality, structure, security and dynamics of complex networks. The research will enable the identification of the vulnerabilities of real-world engineered networks, which can be used either for protection of infrastructural networks or for destruction of functions of pathological and adversarial networks. The research will also provide guidance to design more robust engineered networks. Broader Impacts: The proposed research requires tools from statistical and nonlinear physics, mathematics, control and industrial engineering, and scientific computing. Besides providing a solid mathematical foundation for the problem of network robustness and security, the proposed work will be a demonstration of how interdisciplinary approach can lead to solutions to realistic problems of significant societal concern, due to the ubiquity and extreme importance of complex engineered networks to the modern infrastructure in the US. The research will also create an exciting interdi
|Effective start/end date||9/15/10 → 8/31/13|
- National Science Foundation (NSF): $310,000.00
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