Event-Triggered Model Predictive Control for Multiagent Systems With Communication Constraints

Abstract

This article is concerned with the problem of distributed model predictive control (DMPC) for second-order multiagent systems under event-triggered technique and logarithm quantized communication for a directed topological graph. Considering the limitation of communication bandwidth, a new bounded logarithm quantized communication strategy is proposed to preprocess the information before its transmission, thus reducing the influence of quantization error on the final convergence state. In order to decrease the frequency of control law update and reduce the power consumption, a distributed event-triggered rule is designed to decide when to transmit the information and when to optimize the model predictive control, in which trigger function synthesizes three factors, namely, predictive step, saturation of quantizer, and event-triggered error related with quantized error. The optimal control sequence of DMPC guides the update of controller between two triggering instants. The relationship among the quantization level, event-triggered parameters, and Laplacian matrix is established. Conditions are presented to ensure that all leaders asymptotically converge to a designed formation configuration, while all followers reach to the convex hull of them. Finally, an example is given to illustrate the effectiveness of the proposed methods.