Search

This paper considers the consensus problem of discrete-time switched linear multi-agent systems under a fixed communication topology. An observer-based protocol with sampled data is proposed for solving such a problem. Assume that the digraph has a directed spanning tree and that all switched subsystem of each agent are stabilisable and detectable. It is shown that the proposed protocol solves the consensus problem when the sampling period is sufficiently small and the average dwell time of the switching signal is sufficiently large. Moreover, to reduce the conservatism, a sufficient condition for consensus is obtained to design the feedback gain matrices and the observer gain matrices by linear matrix inequalities, which guarantee that consensus can be achieved when the sampling period and the average dwell time are in the general case. Finally, the effectiveness of the theoretical results is demonstrated through an example. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper studies consensus in linear multi-agent systems with current and sampled partial relative states. A distributed linear consensus protocol is designed, where both current and sampled relative states are utilized. A necessary and sufficient condition for consensus in this setting is established. The notion of the consensus region is then introduced and analyzed for third-order systems, provided that each agent can only know its relative positions and velocities. It is shown that the consensus regions are stable to control gains and sampling period. Additionally, how to choose the control gains and the sampling period is given for consensus in third-order systems. Finally, an example is given to verify and illustrate the analysis. [ABSTRACT FROM AUTHOR]

Modeling and stability analysis of memristor-based neural networks (MNNs) are the premise of designs and applications. Different from most previous research, delayed MNNs are described by continuous differential equations with $2^{2n^{2}+n}$ variables where the memductances of memristors are continuously dependent on the fluxes. Both system delays and input delays are considered, and the delays and their derivatives may vary in intervals whose lower bounds are not restricted to be zero. The systems are further reduced to continuous-time neural networks (NNs) with interval matrix uncertainties, and a unified method is developed to solve the stability of delayed NNs and MNNs. Stability criteria are obtained for delayed MNNs by augmented Lyapunov functionals, Wirtinger-based integral inequality, reciprocally convex approach, and linear matrix inequalities. In the end, two numerical simulations are used to demonstrate the validity of our theorems. [ABSTRACT FROM AUTHOR]