Search for: All records

The platooning of connected and automated vehicles (CAVs) is expected to have a transformative impact on road transportation, e.g., enhancing highway safety, improving traffic utility, and reducing fuel consumption. Requiring only local information, distributed control schemes are scalable approaches to the coordination of multiple CAVs without using centralized communication and computation. From the perspective of multi-agent consensus control, this paper introduces a decomposition framework to model, analyze, and design the platoon system. In this framework, a platoon is naturally decomposed into four interrelated components, i.e., 1) node dynamics, 2) information flow network, 3) distributed controller, and 4) geometry formation. The classic model of each component is summarized according to the results of the literature survey; four main performance metrics, i.e., internal stability, stability margin, string stability, and coherence behavior, are discussed in the same fashion. Also, the basis of typical distributed control techniques is presented, including linear consensus control, distributed robust control, distributed sliding mode control, and distributed model predictive control. 

In wireless networked control systems, ensuring predictable communication link reliabilities among sensors, controllers, and actuators is critical. In such scenarios, different data gathered at the application layer of each sender require different packet delivery ratios (i.e., reliabilities). The lower layers try to accommodate these requests by first mapping each of them into a service level and then deliver the associated data packets to the receiver at the mapped service level. Due to resource constraints and maintenance overhead, the number of supported service levels is usually limited. An important question is then how to determine the set of service levels to maintain and how to map each request to an appropriate service level, such that the requested reliabilities are guaranteed and the total cost of mapping is minimized? We formally formulate this as an optimal request clustering problem since each service level acts as a cluster and can host multiple requests. In particular, we formulate the Migratory Clustering Problem and the Non-Migratory Clustering Problem, depending on whether a request can migrate from one service level to another after its initial assignment. We propose two optimal algorithms to solve both problems.