Efficient quantization for average consensus

TL;DR: In this article, a distributed consensus protocol is proposed based on sampled measurements, which is robust to the logarithmic quantization, i.e. all the states of the agents are uniformly bounded and the gap between the state of each agent and the average value of the initial conditions converges to zero as the density of quantization levels goes to infinity.

TL;DR: A distributed consensus protocol is proposed based on dynamic encoding and decoding and it is shown that for a connected network, as long as the time delays are bounded, the average consensus can be achieved with a finite-level quantizer.

TL;DR: It is proved that under the consensus algorithm designed, the leader-following consensus is achievable and the quantizers equipped for the multi-agent systems can never be saturated.

TL;DR: It is proposed that a LTI time-delay system via the linear state feedback and quantization is quadratic stabilization if and only if the corresponding uncertain auxiliary system is quadRatic stabilization under the case of the single control input or the multiple control inputs.

TL;DR: This paper forms a control problem with a communication channel connecting the sensor to the controller, and provides upper and lower bounds on the channel rate required to achieve different control objectives.

TL;DR: A new control design methodology is proposed, which relies on the possibility of changing the sensitivity of the quantizer while the system evolves, which yields global asymptotic stability.

TL;DR: This work proposes a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximum-likelihood estimate of the parameters, and shows that it works in a network with dynamically changing topology, provided that the infinitely occurring communication graphs are jointly connected.

TL;DR: In this article, the authors review the results available in the literature on data-rate-limited control for linear systems and show how fundamental tradeoffs between the data rate and control goals, such as stability, mean entry times, and asymptotic state norms, emerge naturally.