This paper addresses the problem of simultaneous distributed state estimation and control of linear systems with linear state feedback, subjected to process and measurement noise, under the constraints of quantized and rate-limited network data transmission. In the set-up adopted, sensors and actuators communicate through a network with a strongly connected topology. Unlike the case of centralized linear systems, for which the separation principle holds, the above practical assumption prevents the separate design of observers and controller because each of the nodes does not necessarily have access to the control inputs generated at all the other nodes. We derive a linear distributed Luenberger observer and derive a set of sufficient conditions that guarantee ultimate boundedness of the estimation error and system state vectors, with bounds that depend on the L1 norm of the noise signals and the number of bits used in the transmissions. A numerical example illustrates the performance and effectiveness of the proposed algorithm in controlling a network of open-loop unstable systems.

The paper addresses the problem of designing distributed observers for discrete linear time-invariant (LTI) systems with distributed sensor nodes subjected to bounded measurement noise. A solution is proposed in terms of a distributed LTI Luenberger observer, thus departing from common linear time-varying solutions rooted in consensus-based distributed estimation techniques, and dispensing with the need for the exchange of covariance matrices. It is shown, under the conditions of collective observability, strong connectivity of the sensor communication network, and invertibility of the state transition matrix, that the resulting observer yields ultimate boundedness of the estimation error. A design example is given where the asymptotic performance of the proposed observer is shown to be similar to that obtained using a time-varying distributed Kalman filtering approach.

This chapter introduces an event-driven, logic-based communication system for decentralized control of a network of nonlinear systems (agents) with the objective of driving their outputs along predefined paths at desired speeds, while holding a desired formation pattern compatible with the paths. An extended cooperative path following (CPF) framework is adopted where communications among agents take place at discrete time instants, instead of continuously. The communication system takes into account explicitly the topology of the communications network, the fact that communications are discrete, and the cost of exchanging information among agents. The theoretical framework adopted allows for the consideration of communication losses and bounded delays. Conditions are derived under which the resulting multi-agent closed-loop system is input-to-state stable, that is stable and with guaranteed levels of performance in the presence of bounded external disturbances and measurement noise. The set-up derived is used to solve the problem of CPF control of multiple underactuated autonomous marine vehicles. The results of experimental field tests with a group of marine vehicles are presented and discussed.