Probabilistic Sufficient Conditions for Prediction-Based Stabilization of Linear Systems With Random Input Delay

This letter focuses on the prediction-based stabilization of a linear system subject to a random input delay. Modeling the delay as a finite-state Markov process, it proves that a constant time-horizon prediction enables robust compensation of the delay, provided the horizon prediction is sufficiently close to the delay values in average. Simulation results emphasize the practical relevance of this condition.