Multi-agent estimation and control of cyber-physical systems

dc.contributor.authorAlam, S. M. Shafiul
dc.description.abstractA cyber-physical system (CPS) typically consists of networked computational elements that control physical processes. As an integral part of CPS, the widespread deployment of communicable sensors makes the task of monitoring and control quite challenging especially from the viewpoint of scalability and complexity. This research investigates two unique aspects of overcoming such barriers, making a CPS more robust against data explosion and network vulnerabilities. First, the correlated characteristics of high-resolution sensor data are exploited to significantly reduce the fused data volume. Specifically, spatial, temporal and spatiotemporal compressed sensing approaches are applied to sample the measurements in compressed form. Such aggregation can directly be used in centralized static state estimation even for a nonlinear system. This approach results in a remarkable reduction in communication overhead as well as memory/storage requirement. Secondly, an agent based architecture is proposed, where the communicable sensors (identified as agents) also perform local information processing. Based on the local and underdetermined observation space, each agent can monitor only a specific subset of global CPS states, necessitating neighborhood information exchange. In this framework, we propose an agent based static state estimation encompassing local consensus and least square solution. Necessary bounds for the consensus weights are obtained through the maximum eigenvalue based convergence analysis and are verified for a radial power distribution network. The agent based formulation is also applied for a linear dynamical system and the consensus approach is found to exhibit better and more robust performance compared to a diffusion filter. The agent based Kalman consensus filter (AKCF) is further investigated, when the agents can choose between measurements and/or consensus, allowing the economic allocation of sensing and communication tasks as well as the temporary omission of faulty agents. The filter stability is guaranteed by deriving necessary consensus bounds through Lyapunov stability analysis. The states dynamically estimated from AKCF can be used for state-feedback control in a model predictive fashion. The effect of lossy communication is investigated and critical bounds on the link failure rate and the degree of consensus that ensure stability of the agent based control are derived and verified via simulations.en_US
dc.description.advisorBalasubramaniam Natarajanen_US
dc.description.degreeDoctor of Philosophyen_US
dc.description.departmentElectrical and Computer Engineeringen_US
dc.description.sponsorshipNational Science Foundation (Division of Computer and Network Systems) Grant # 1136040en_US
dc.publisherKansas State Universityen
dc.subjectCyber-physical systemen_US
dc.subjectMulti-agent systemen_US
dc.subjectCompressed sensingen_US
dc.subjectKalman consensus filteren_US
dc.subjectModel predictive controlen_US
dc.subjectLyapunov stability analysisen_US
dc.subject.umiElectrical Engineering (0544)en_US
dc.titleMulti-agent estimation and control of cyber-physical systemsen_US


Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
4.76 MB
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
1.62 KB
Item-specific license agreed upon to submission