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Switching Predictive Control Using Reconfigurable State-Based Model

Authors:

Tony GivargisAuthors Info & Claims

ACM Transactions on Design Automation of Electronic Systems (TODAES), Volume 24, Issue 1

Article No.: 2, Pages 1 - 21

https://doi.org/10.1145/3267126

Published: 19 November 2018 Publication History

Abstract

Advanced control methodologies have helped the development of modern vehicles that are capable of path planning and path following. For instance, Model Predictive Control (MPC) employs a predictive model to predict the behavior of the physical system for a specific time horizon in the future. An optimization problem is solved to compute optimal control actions while handling model uncertainties and nonlinearities. However, these prediction routines are computationally intensive and the computational overhead grows with the complexity of the model. Switching MPC addresses this issue by combining multiple predictive models, each with a different precision granularity. In this artcle, we proposed a novel switching predictive control method based on a model reduction scheme to achieve various model granularities for path following in autonomous vehicles. A state-based model with tunable parameters is proposed to operate as a reconfigurable predictive model of the vehicle. A runtime switching algorithm is presented that selects the best model using machine learning. We employed a metric that formulates the tradeoff between the error and computational savings due to model reduction. Our simulation results show that the use of the predictive model in the switching scheme as opposed to single granularity scheme, yields a 45% decrease in execution time in tradeoff for a small 12% loss in accuracy in prediction of future outputs and no loss of accuracy in tracking the reference trajectory.

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Cited By

Chen GYao JHu HGao ZHe LZheng X(2022)Design and experimental evaluation of an efficient MPC-based lateral motion controller considering path preview for autonomous vehiclesControl Engineering Practice10.1016/j.conengprac.2022.105164123(105164)Online publication date: Jun-2022
https://doi.org/10.1016/j.conengprac.2022.105164
Givargis T(2021)GravityProceedings of the 26th Asia and South Pacific Design Automation Conference10.1145/3394885.3431514(715-721)Online publication date: 18-Jan-2021
https://dl.acm.org/doi/10.1145/3394885.3431514
Honda KOkuda HSuzuki T(2020)Multi-task Model Predictive Control based on Continuation with Intermediate Mode2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC45102.2020.9294663(1-7)Online publication date: 20-Sep-2020
https://doi.org/10.1109/ITSC45102.2020.9294663

Index Terms

Switching Predictive Control Using Reconfigurable State-Based Model
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Computing methodologies
  1. Machine learning
  2. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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Published In

cover image ACM Transactions on Design Automation of Electronic Systems

ACM Transactions on Design Automation of Electronic Systems Volume 24, Issue 1

January 2019

309 pages

ISSN:1084-4309

EISSN:1557-7309

DOI:10.1145/3293467

Editor:
Naehyuck Chang
Korea Advanced Institute of Science and Technology, Korea

Issue’s Table of Contents

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 19 November 2018

Accepted: 01 August 2018

Revised: 01 August 2018

Received: 01 March 2018

Published in TODAES Volume 24, Issue 1

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Cited By

Chen GYao JHu HGao ZHe LZheng X(2022)Design and experimental evaluation of an efficient MPC-based lateral motion controller considering path preview for autonomous vehiclesControl Engineering Practice10.1016/j.conengprac.2022.105164123(105164)Online publication date: Jun-2022
https://doi.org/10.1016/j.conengprac.2022.105164
Givargis T(2021)GravityProceedings of the 26th Asia and South Pacific Design Automation Conference10.1145/3394885.3431514(715-721)Online publication date: 18-Jan-2021
https://dl.acm.org/doi/10.1145/3394885.3431514
Honda KOkuda HSuzuki T(2020)Multi-task Model Predictive Control based on Continuation with Intermediate Mode2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC45102.2020.9294663(1-7)Online publication date: 20-Sep-2020
https://doi.org/10.1109/ITSC45102.2020.9294663

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