When classical predictors fail: an exact network outflowpredictor for heterogeneous multi-agent systems with communication delays

Abstract

This paper develops an information outflow predictor-feedback framework for the exact compensation of constant but nonuniform communication delays in the output synchronization problem of discrete-time heterogeneous multi-agent systems. The delays considered here act exclusively on communication channels between neighboring agents, rather than on the plant state, input or output, and thus fall outside the scope of classical predictor-feedback formulations. We show that standard approaches based on time inversion and variational construction of predictor states are insufficient, since even for simple directed acyclic communication graphs the required prediction horizon exceeds the delay window. We further illustrate that exact predictor-based compensation is achievable only in the discrete-time setting, while in continuous-time configuration the predictor state becomes non-computable due to the need for a continuum inaccessible future neighboring-agent information. Motivated by these observations, we propose a distributed prediction architecture in which a prediction-based distributed observer exactly reconstructs the flow of information exchanged between agents across the network. Furthermore, the layer-by-layer information flow eliminates the effect of communication delays after a finite number of steps. Based on this structure, we design prediction-based distributed state-feedback and dynamic output-feedback controllers by combining standard feedback designs with prediction-based distributed observers serving as feedforward components, such that the output of each agent asymptotically tracks the trajectory generated by the exosystem, thereby ensuring output synchronization.