As sensing and communication technologies become faster, smaller and cheaper, they become ever more integrated with common infrastructure and utilities in our world. Widely available to the public, the new technology can be leveraged to create more advanced systems where smart devices are linked with users and operators by real-time communication networks. Modern examples include traffic systems, the power grid, smart buildings, and automated factory environments. The coordination between users and operators can dramatically expand the efficiency of these networked infrastructure systems, while reducing problems of congestion and high demand. This award supports fundamental research to provide needed knowledge and techniques for the development of automatic control algorithms that can meet these goals with guaranteed performance. The coordination of multiple users and operators is very challenging as interactions occur only locally, user/operator needs can quickly change due to unpredicted events, the potential limitations of service over periods of high demand, and the unreliability of the smart device operation. This research will bridge existing theoretical tools and develop new ones that can be used to guide the design of novel robust algorithms. Therefore, results from this research will benefit the U.S. economy and society. This research involves several disciplines including control theory, distributed computation, optimization, and robotics. The multi-disciplinary approach will help broaden participation of underrepresented groups in research and positively impact engineering education.
This project's research objectives are the design of distributed coordination and re-routing algorithms for the control networked systems subject to constraints in a wide variety of scenarios. The research plan is articulated along the following thrusts: (i) The design of aggregation-based robust demand-response algorithms for load shifting: While aggregation can help achieve load-shifting objectives, it is key to understand its robustness properties with respect to noise, time-varying interactions, and delays. (ii) The design of distributed load re-balancing and re-routing algorithms: Heterogeneous constraints can have a dramatic impact on distributed algorithm behavior, breaking the natural multi-agent interaction flow and leading to congestion. We aim to study how re-distribution can help alleviate congestion. (iii) The integration of demand-response algorithms with distributed routing and balancing algorithms. Nonlinear stability tools will be used to analyze the robustness of the algorithms by bridging existing gaps between the smooth theory of height Lyapunov functions, set-valued map theory, and contractive analysis.
