Consensus control for multi-agent systems with unknown faded neighborhood information via iterative learning scheme

In this paper, the consensus problem of multi-agent systems with unknown faded neighborhood information is addressed using the iterative learning control method. Considering that information exchange in wireless networks may be disturbed by unknown fading effects and unknown additive noise, it is significant to realize accurate consensus tracking of each agent to a given leader under the contaminated information. Unlike the traditional mechanism of correcting unknown faded neighborhood information by estimating the statistical characteristics of random fading variables, we introduce test signals to correct the trajectory signals of each agent. As no estimation mechanism is involved, the storage and computational burden of the whole system are greatly reduced. Based on a classic distributed structure and a novel correction mechanism, two novel distributed learning consensus control schemes are constructed. The consensus results of multi-agent systems under the two learning control schemes are discussed in detail using mathematical analysis tools. Finally, the multi-pendulum network system is simulated to verify the theoretical results.