Robust reduced-order model predictive control using peak-to-peak analysis of filtered signals
arXiv:2511.03002 · doi:10.1016/j.ejcon.2026.101569
Abstract
We address the design of a model predictive control (MPC) scheme for large-scale linear systems using reduced-order models (ROMs). Our approach uses a ROM, leverages tools from robust control, and integrates them into an MPC framework to achieve computational tractability with robust constraint satisfaction. Our key contribution is a method to obtain guaranteed bounds on the predicted outputs of the full-order system by predicting a (scalar) error-bounding system alongside the ROM. This bound is then used to formulate a robust ROM-based MPC that guarantees constraint satisfaction and robust performance. Our method is developed step-by-step by (i) analysing the error, (ii) bounding the peak-to-peak gain, an (iii) using filtered signals. We demonstrate our method on a 100-dimensional mass-spring-damper system, achieving over four orders of magnitude reduction in conservatism relative to existing approaches.
This is the accepted version of the paper in the European Journal of Control, 2026. This version contains more explanation for the dynamic filters, the computational demand, and two remarks addressing receding horizon implementation and incorperation of tube feedbacks