Riccati-ZORO decomposes joint optimization of nominal trajectory and ellipsoidal uncertainty tube into two subproblems, lowering complexity from O(n_x^6) to O(n_x^3) for linear feedback.
Stochastic model predictive control: An overview and perspectives for future research,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.OC 2representative citing papers
In linear systems with parametric uncertainty and Gaussian noise, MPC policy dependence on posterior covariance peaks at high uncertainty and vanishes as it contracts, with the dual controller outperforming certainty-equivalent MPC on both regulation performance and model accuracy.
citing papers explorer
-
Riccati-ZORO: An efficient algorithm for heuristic online optimization of internal feedback laws in robust and stochastic model predictive control
Riccati-ZORO decomposes joint optimization of nominal trajectory and ellipsoidal uncertainty tube into two subproblems, lowering complexity from O(n_x^6) to O(n_x^3) for linear feedback.
-
The Separation Principle and the Dual-Certainty Equivalence Gap in Model Predictive Control
In linear systems with parametric uncertainty and Gaussian noise, MPC policy dependence on posterior covariance peaks at high uncertainty and vanishes as it contracts, with the dual controller outperforming certainty-equivalent MPC on both regulation performance and model accuracy.