Bayesian model predictive control: Efficient model exploration and regret bounds using posterior sampling
OPEN ACCESS
Loading...
Author / Producer
Date
2020-06-10
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
Tight performance specifications in combination with operational constraints make model predictive control (MPC) the method of choice in various industries. As the performance of an MPC controller depends on a sufficiently accurate objective and prediction model of the process, a significant effort in the MPC design procedure is dedicated to modeling and identification. Driven by the increasing amount of available system data and advances in the field of machine learning, data-driven MPC techniques have been developed to facilitate the MPC controller design. While these methods are able to leverage available data, they typically do not provide principled mechanisms to automatically trade off exploitation of available data and exploration to improve and update the objective and prediction model. To this end, we present a learning-based MPC formulation using posterior sampling techniques, which provides finite-time regret bounds on the learning performance while being simple to implement using off-the-shelf MPC software and algorithms. The performance analysis of the method is based on posterior sampling theory and its practical efficiency is illustrated using a numerical example of a highly nonlinear dynamical car-trailer system.
Permanent link
Publication status
published
Book title
Proceedings of the 2nd Conference on Learning for Dynamics and Control
Journal / series
Volume
120
Pages / Article No.
455 - 464
Publisher
PMLR
Event
2nd Conference on Learning for Dynamics and Control (L4DC 2020)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09563 - Zeilinger, Melanie / Zeilinger, Melanie
Notes
Funding
157601 - Safety and Performance for Human in the Loop Control (SNF)