Tokyo Institute of Technology · Engineering
Professor Hideaki Ishii's research lab specializes in networked control systems, distributed algorithms, and stochastic optimization, with a focus on stabilizing systems under limited communication resources. The lab investigates innovative control strategies—such as dwell-time switching and probabilistic algorithms—for linear and nonlinear systems that lack a single quadratic Lyapunov function, addressing nonconvexity challenges in system design. A significant part of the research is dedicated to distributed computation of PageRank-like metrics, drawing connections to multi-agent consensus problems and enabling scalable, decentralized solutions for large-scale networks. The lab also explores the theoretical foundations of information exchange in control and web ranking systems, emphasizing robustness, convergence, and practical implementation under data rate constraints.
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The use of networks in control systems to connect controllers and sensors/actuators has become common practice in many applications. This new technology has also posed a theoretical control problem of how to use the limited data rate of the network effectively. In this paper, we consider a system where its sensor and actuator are connected by a finite data rate channel. A design method to stabilize a continuous-time, linear plant using a switching controller is proposed. In particular, to preven
In the search engine of Google, the PageRank algorithm plays a crucial role in ranking the search results. The algorithm quantifies the importance of each web page based on the link structure of the web. We first provide an overview of the original problem setup. Then, we propose several distributed randomized schemes for the computation of the PageRank, where the pages can locally update their values by communicating to those connected by links. The main objective of the paper is to show that t
PageRank is an algorithm introduced in 1998 and used by the Google Internet search engine. It assigns a numerical value to each element of a set of hyperlinked documents (that is, Web pages) within the World Wide Web with the purpose of measuring the relative importance of each page [1]. The key idea in the algorithm is to give a higher PageRank value to Web pages that are visited often by Web surfers. Google describes PageRank as: "PageRank reflects our view of the importance of Web pages by co
In this paper, we consider the design of globally asymptotically stabilizing state-dependent switching rules for multimodal systems, first restricting attention to linear time-invariant (LTI) systems with only two states for the switch, and then generalizing the results to multimodal LTI systems and to nonlinear systems. In all cases, the systems considered do not allow the construction of a single quadratic Lyapunov function and, hence, fall in the class of problems that require multiple Lyapun
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