Real-time optimization and model predictive control for aerospace and automotive applications

In recent years control methods based on real-time optimization (RTO) such as model predictive control (MPC) have been investigated for a significant number of applications in the automotive and aerospace (A&A) domains. This paper provides a tutorial overview of RTO in automotive and aerospace a...

Full description

Saved in:
Bibliographic Details
Published in:2018 Annual American Control Conference (ACC) pp. 2392 - 2409
Main Authors: Di Cairano, Stefano, Kolmanovsky, Ilya V.
Format: Conference Proceeding
Language:English
Published: AACC 01.06.2018
Subjects:
Actuators
Aerospace control
Automotive engineering
Engines
Magnetic materials
Optimization
Photonic crystals
ISSN:2378-5861
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In recent years control methods based on real-time optimization (RTO) such as model predictive control (MPC) have been investigated for a significant number of applications in the automotive and aerospace (A&A) domains. This paper provides a tutorial overview of RTO in automotive and aerospace applications, with particular focus on MPC which is probably the most largely investigated method. First, we review the features that make RTO appealing for A&A applications. Then, due to the model-based nature of these control methods, we describe the key first principle models and opportunities that these provide for RTO. Next, we detail the key steps and guidelines of the MPC design process which are tailored to A&A systems. Finally, we discuss numerical algorithms for implementing RTO, and their suitability for implementation in embedded computing platforms to in A&A domains.
ISSN:2378-5861
DOI:10.23919/ACC.2018.8431585