2015/11/22 by Yinlam Chow, Marco Pavone, Chow, Yin-Lam +1
Engineering · #Advanced Control Systems Optimization #FOS: Mathematics #Fault Detection and Control Systems #Optimization and Control (math.OC) #Stability and Control of Uncertain Systems
paper · pdf · doi:10.48550/arxiv.1511.06981
openalex publication_date 2015/11/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we present a framework for risk-averse model predictive control\n(MPC) of linear systems affected by multiplicative uncertainty. Our key\ninnovation is to consider time-consistent, dynamic risk metrics as objective\nfunctions to be minimized. This framework is axiomatically justified in terms\nof time-consistency of risk preferences, is amenable to dynamic optimization,\nand is unifying in the sense that it captures a full range of risk assessments\nfrom risk-neutral to worst case. Within this framework, we propose and analyze\nan online risk-averse MPC algorithm that is provably stabilizing. Furthermore,\nby exploiting the dual representation of time-consistent, dynamic risk metrics,\nwe cast the computation of the MPC control law as a convex optimization problem\namenable to implementation on embedded systems. Simulation results are\npresented and discussed.\n