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Certainty-Equivalence Model Predictive Control: Stability, Performance, and Beyond
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DOI：10.1109/tac.2026.3661789.png)
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Abstract
En 中文
Handling model mismatch is a common challenge in model predictive control (MPC). While robust MPC is effective, its conservatism often makes it less desirable. Certainty-equivalence MPC (CE-MPC), which uses the nominal model, offers an appealing alternative due to its design simplicity and low computational cost. This article investigates CE-MPC for uncertain nonlinear systems with multiplicative parametric uncertainty and input constraints that are inactive at the steady state. The primary contributions are twofold. First, a novel perturbation analysis of the MPC value function is provided, without assuming the Lipschitz continuity of the stage cost, better tailored to the widely used quadratic costs, and having broader applicability in value function approximation, learning-based MPC, and performance-driven MPC design. Second, the stability and performance analysis of CE-MPC is provided, quantifying the suboptimality of CE-MPC compared to the infinite-horizon optimal controller with perfect model knowledge. The results provide insights into how the prediction horizon and model mismatch jointly affect stability and the worst-case performance. Furthermore, the general results are specialized to linear quadratic control, and a competitive-ratio bound is derived, serving as the first competitive-ratio bound for MPC of uncertain linear systems with input constraints and multiplicative uncertainty.
Keywords:
Model predictive control
optimal control
performance analysis
perturbation analysis
uncertain systems
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W