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   Decentralised minimum-time consensus; ; et al in Automatica (2013), 49(5), 1227-1235 We consider the discrete-time dynamics of a network of agents that exchange information according to a nearest-neighbour protocol under which all agents are guaranteed to reach consensus asymptotically ... [more ▼] We consider the discrete-time dynamics of a network of agents that exchange information according to a nearest-neighbour protocol under which all agents are guaranteed to reach consensus asymptotically. We present a fully decentralised algorithm that allows any agent to compute the final consensus value of the whole network in finite time using the minimum number of successive values of its own state history. We show that the minimum number of steps is related to a Jordan block decomposition of the network dynamics, and present an algorithm to compute the final consensus value in the minimum number of steps by checking a rank condition of a Hankel matrix of local observations. Furthermore, we prove that the minimum number of steps is related to graph theoretical notions that can be directly computed from the Laplacian matrix of the graph and from the minimum external equitable partition. [less ▲] Detailed reference viewed: 205 (4 UL)Distributed Kalman Filter with minimum-time covariance computation; ; et al in The proceedings of the IEEE 52nd Annual Conference on Decision and Control (2013) This paper considerably improves the well-known Distributed Kalman Filter (DKF) algorithm by Olfati-Saber (2007) by introducing a novel decentralised consensus value computation scheme, using only local ... [more ▼] This paper considerably improves the well-known Distributed Kalman Filter (DKF) algorithm by Olfati-Saber (2007) by introducing a novel decentralised consensus value computation scheme, using only local observations of sensors. It has been shown that the state estimates obtained in [8] and [9] approaches those of the Central Kalman Filter (CKF) asymptotically. However, the convergence to the CKF can sometimes be too slow. This paper proposes an algorithm that enables every node in a sensor network to compute the global average consensus matrix of measurement noise covariance in minimum time without accessing global information. Compared with the algorithm in [8], our theoretical analysis and simulation results show that the new algorithm can offer improved performance in terms of time taken for the state estimates to converge to that of the CKF. [less ▲] Detailed reference viewed: 120 (0 UL) |
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