Distributed bounded-error state estimation for partitioned systems based on practical robust positive invariance

We propose a partition-based state estimator for linear discrete-time systems composed by coupled subsystems affected by bounded disturbances. The architecture is distributed in the sense that each subsystem is equipped with a local state estimator that exploits suitable pieces of information from parent subsystems. Moreover, differently from methods based on moving horizon estimation, our approach does not require the on-line solution to optimization problems. Our state-estimation scheme, that is based on the notion of practical robust positive invariance developed in Rakovic 2011, also guarantees satisfaction of constraints on local estimation errors and it can be updated with a limited computational effort when subsystems are added or removed

Plug-and-Play Fault Detection and control-reconfiguration for a class of nonlinear large-scale constrained systems

This paper deals with a novel Plug-and-Play (PnP) architecture for the control and monitoring of Large-Scale Systems (LSSs). The proposed approach integrates a distributed Model Predictive Control (MPC) strategy with a distributed Fault Detection (FD) architecture and methodology in a PnP framework. The basic concept is to use the FD scheme as an autonomous decision support system: once a fault is detected, the faulty subsystem can be unplugged to avoid the propagation of the fault in the interconnected LSS. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in. PnP design of local controllers and detectors allow these operations to be performed safely, i.e. without spoiling stability and constraint satisfaction for the whole LSS. The PnP distributed MPC is derived for a class of nonlinear LSSs and an integrated PnP distributed FD architecture is proposed. Simulation results in two paradigmatic examples show the effectiveness and the potential of the general methodology

A distributed attack detection method for multi-agent systems governed by consensus-based control

The paper considers the problem of detecting cyber-attacks occurring in communication networks for distributed control schemes. A distributed methodology is proposed to detect the presence of malicious attacks aimed at compromising the stability of large-scale interconnected systems and multi-agent systems governed by consensus-based controllers. Only knowledge of the local model is required. The detectability properties of the proposed method are analyzed. A class of undetectable attacks is identified. Preliminary simulation results show the effectiveness of the proposed approach

Distributed Cyber-Attack Detection in the Secondary Control of DC Microgrids

The paper considers the problem of detecting cyber-attacks occurring in communication networks typically used in the secondary control layer of DC microgrids. The proposed distributed methodology allows for scalable monitoring of a microgrid and is able to detect the presence of data injection attacks in the communications among Distributed Generation Units (DGUs) - governed by consensus-based control - and isolate the communication link over which the attack is injected. Each local attack detector requires limited knowledge regarding the dynamics of its neighbors. Detectability properties of the method are analyzed, as well as a class of undetectable attacks. Some results from numerical simulation are presented to demonstrate the effectiveness of the proposed approach

Containment Control in Mobile Networks

(c) 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Digital Object Identifier: 10.1109/TAC.2008.930098In this paper, the problem of driving a collection of mobile robots to a given target destination is studied. In particular, we are interested in achieving this transfer in an orderly manner so as to ensure that the agents remain in the convex polytope spanned by the leader-agents, while the remaining agents, only employ local interaction rules. To this aim we exploit the theory of partial difference equations and propose hybrid control schemes based on stop-go rules for the leader-agents. Non-Zenoness, liveness and convergence of the resulting system are also analyzed

Plug and Play Distributed Model Predictive Control Based on Distributed Invariance and Optimization

Abstract—This paper presents a method for plug-and-play distributed MPC of a network of interacting linear systems. The previously introduced idea of plug and play control addresses the challenge of performing network changes in the form of subsystems that are joining or leaving the network during closed-loop operation, while maintaining stability and constraint satisfaction. This work extends these ideas to an iterative distributed MPC scheme for systems with strong coupling by employing a recently proposed method for distributed MPC that takes the coupling dynamics into account in the form of time-varying terminal sets and distributed optimization. A distributed synthesis procedure for the update of the local control laws is proposed together with a transition scheme preparing the system for the upcoming modifications. This enables automatic plug-and-play operation, including rejection if the new network topology is infeasible. Both the synthesis and online control are entirely distributed and are only based on local information on the subsystems and their coupled neighbors. Finally, the proposed scheme is applied to the problem of frequency control in a power network

Stochastic Fault Detection in a plug-and-play scenario

This paper proposes a novel stochastic Fault Detection (FD) approach for the monitoring of Large-Scale Systems (LSSs) in a Plug-and-Play (PnP) scenario. The proposed architecture considers stochastic bounds on the measurement noises and modeling uncertainties, providing probabilistic time-varying FD thresholds with guaranteed false alarms probability levels. The monitored LSS consists of several interconnected subsystems and the designed FD architecture is able to manage plugging-in of novel subsystems and un-plugging of existing ones. Moreover, the proposed PnP approach can perform the unplugging of faulty subsystems in order to avoid the propagation of faults in the interconnected LSS. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in. The reconfiguration processes involve only local operations of neighboring subsystems, thus allowing a scalable architecture. A consensus approach is used for the estimation of variables shared among more than one subsystem; a method is proposed to define the time-varying consensus weights in order to allow PnP operations and to minimize at each step the variance of the uncertainty of the FD thresholds. Simulation results on a Power Network application show the effectiveness of the proposed approach