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Robust quantized consensus of discrete multi-agent systems under input saturation
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DOI：10.1016/j.jfranklin.2018.11.033.png)
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Abstract
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In this paper, we consider the quantized consensus problem of multiple discrete-time integrator agents which suffer from input saturation. As agents transmit state information through communication networks with limited bandwidth, the states of agents have to be quantized into a finite number of bits before transmission. To handle this quantized consensus problem, we introduce an internal time-varying saturation function into the controllers of all agents and ensure that the range of the state of each agent can be known in advance by its neighboring agents. Based on such shared state range information, we construct a quantized consensus protocol which implements a finite-bit quantization strategy to all states of agents and can guarantee the achievement of the asymptotic consensus under any given input saturation threshold. Such desired consensus can be guaranteed at as low bit rate as 1 bit per time step for each agent. Moreover, we can place an upper bound on the convergence rate of the consensus error of agents. We further improve that quantized consensus protocol to a robust version whose parameters are determined with only an upper bound on the number of agents and does not require any more global information of the inter-agent network. Simulations are done to confirm the effectiveness of our quantized consensus protocols. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Keywords:
LEADER-FOLLOWING CONSENSUS
FEEDBACK STABILIZATION
SUBJECT
AGENTS
COORDINATION
NETWORKS
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