Robust reduced-order model predictive control using peak-to-peak analysis of filtered signals

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.