State space models vs. multi-step predictors in predictive control: Are state space models complicating safe data-driven designs?

2022/03/29 by Johannes Köhler, Kim P. Wabersich, Köhler, Johannes +5 · 3 citations
Engineering · #Advanced Control Systems Optimization #Control Systems and Identification #FOS: Electrical engineering #FOS: Mathematics #Fault Detection and Control Systems #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2203.15471

openalex publication_date 2022/03/29 · openalex created_date 2022/04/26 · openalex updated_date 2026/08/01

Abstract

This paper contrasts recursive state space models and direct multi-step predictors for linear predictive control. We provide a tutorial exposition for both model structures to solve the following problems: 1. stochastic optimal control; 2. system identification; 3. stochastic optimal control based on the estimated model. Throughout the paper, we provide detailed discussions of the benefits and limitations of these two model parametrizations for predictive control and highlight the relation to existing works. Additionally, we derive a novel (partially tight) constraint tightening for stochastic predictive control with parametric uncertainty in the multi-step predictor.

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