Distributed Multi-Agent D-Type Iterative Learning Algorithm Research under Communication Interference

Abstract

Iterative learning is based on the continuous iterative updating of model parameters, which does not need an accurate system model, and can realize the control of dynamic systems with high uncertainty characteristics in a limited time. It is widely used in many fields, such as robots, UAVs, transportation, networks, finance and medical treatment. The distributed multi-agent based on iterative learning distributes large-scale data on multiple nodes for parallel processing, which speeds up the learning speed and improves the robustness of the algorithm. However, the multi-agent system depends on the accurate information transmission between individuals to complete the target task. Due to the complexity of the environment and uncertain factors, such as data loss, delay, noise and other interference, it must be considered. In this paper, an iterative learning control protocol based on local data and a D-type iterative learning algorithm are designed for nonlinear time-varying multi-agent systems with communication interference under the premise that the initial state can be reset to zero. Simulation results show that the proposed algorithm can effectively solve the consistency problem and suppress any type of disturbance, which has strong theoretical and practical significance.