Learning-based Distributed Model Predictive Control with State-dependent Uncertainty Using Neural Network

This paper presents a novel learning-based dis-tributed model predictive control strategy addressing the con-sensus problem in multi-agent systems with unknown state- dependent uncertainties. Assuming the availability of input- output data, a neural network-based model is used to describe state-dependent uncertainties. The Lipschitz constant of the neural network is estimated, and an upper bound on the prediction error of the neural network is derived based on this estimation. The multi-agent system is proven to be input- to-state stable concerning the prediction error. A numerical example is introduced to validate the theoretical results of the proposed controller. A comparison with other learning-based controllers illustrates the efficiency of the neural network-based controller. This research contributes to the advancement of learning-based model predictive control in scenarios involving multi-agent systems with inaccurate models.