Adaptive Iterative Learning Control of Multi–Agent Systems Under Iterative Batch Random Missing Faults Based on Neural Networks

For multi-agent systems with missing iteration batches and randomly varying lengths, a data-driven adaptive iterative learning control strategy based on neural networks is proposed. Considering the situation where the iteration batches of the system vary randomly with the number of iterations, first, an iterative batch missing fault model that follows a random probability distribution is designed. Secondly, based on the radial basis function neural network method, a novel data-driven adaptive iterative learning consistency control algorithm is proposed. The stability analysis of the system is strictly performed using the Lyapunov function, and the sufficient conditions for consistency of multi-agent systems are derived through relevant mathematical derivations. This enables multi-agent systems to achieve state consistency even when there are missing faults in the iterative batches. Finally, the feasibility of the algorithm is verified through numerical simulations in a multi-agent system.