Consensus Under Event-Triggered Transmission and Quantization

It is well known that ILC is an effective technique that focuses on improving the performance of systems over a fixed time interval by learning from previous executions (trials, iterations, passes). This control strategy can tackle dynamic systems with a high level of uncertainty in a simple way. As a result, ILC has been widely applied to industrial robots (Arimoto et al. 1984), chemical reactors (Mezghani et al. 2002), input saturation (Tan et al. 2011; Xiong et al. 2016; Xu et al. 2004; Zhang et al. 2015), heat equations (Huang et al. 2013), sampled-data systems (Abidi and Xu 2011), and multi-agent systems (Li and Li 2014; Meng et al. 2015a, b, 2013, 2014; Meng and Moore 2016). For example, in Li and Li (2014) showed that all followers could track the leader uniformly on the finite interval for the consensus problem and keep the desired distance from the leader to achieve the velocity consensus uniformly on [1, T]. In Meng et al. (2014), tackled the formation control problems for multi-agent systems with nonlinear dynamics and switching network topologies. It was shown in Meng et al. (2015a) that these uncertainties of multi-agent systems are dynamically changing not only along the time axis but also along the iteration axis.