When expectation fails: stochastic MPC of linear systems with random input losses

Abstract

We consider stochastic model predictive control (MPC) for constrained linear systems subject to multiplicative binary input uncertainty, motivated by applications such as networked control with packet losses and intermittent actuation. A common approach in this setting replaces the stochastic dynamics with their expectation, yielding tractable formulations that admit standard terminal ingredients and stability guarantees in expectation. We show that such formulations can exhibit structural properties that differ fundamentally from those of deterministic MPC and may be misleading as indicators of realized closed-loop behaviour. In particular, the expected value function is not necessarily monotonic in the prediction horizon, and value function-based inner approximations of the region of attraction may deteriorate as the horizon increases. Furthermore, we establish a probabilistic comparison with certainty-equivalent (optimistic) MPC, showing that the latter can ensure a strictly positive probability of recursive feasibility in situations where stochastic MPC certifies feasibility but fails with probability one. These results highlight inherent limitations of expectation-based stochastic MPC for systems with multiplicative binary uncertainty and motivate a re-examination of how stochasticity is incorporated into constrained predictive control design for such systems.