Loading…

Teaching Predictive Control Using Specification-based Summative Assessments

Including Model Predictive Control (MPC) in the undergraduate/graduate control curriculum is becoming vitally important due to the growing adoption of MPC in many industrial areas. In this paper, we present an overview of the predictive control course taught by the authors at Imperial College London...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-04
Main Authors: McInerney, Ian, Kerrigan, Eric C
Format: Article
Language:English
Subjects:
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator McInerney, Ian
Kerrigan, Eric C
description Including Model Predictive Control (MPC) in the undergraduate/graduate control curriculum is becoming vitally important due to the growing adoption of MPC in many industrial areas. In this paper, we present an overview of the predictive control course taught by the authors at Imperial College London between 2018 and 2021. We discuss how the course evolved from focusing solely on the linear MPC formulation to covering nonlinear MPC and some of its extensions. We also present a novel specification-based summative assessment framework, written in MATLAB, that was developed to assess the knowledge and understanding of the students in the course by tasking them with designing a controller for a real-world problem. The MATLAB assessment framework was designed to provide the students with the freedom to design and implement any MPC controller they wanted. The submitted controllers were then assessed against over 30 variations of the real-world problem to gauge student understanding of design robustness and the MPC topics from the course.
doi_str_mv 10.48550/arxiv.2202.00157
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2624801652</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2624801652</sourcerecordid><originalsourceid>FETCH-LOGICAL-a957-e1ea81cd5b5a69c33caa4ccb82ec1e154ed7c4febdfdee8895040a6d541f277d3</originalsourceid><addsrcrecordid>eNotjctqwzAQRUWh0JDmA7ozdG1XGmlseRlMXzTQQtx1GEvjViG2U8sO_fw-V3dxDucKcaVkZiyivKHxM5wyAAmZlAqLM7EArVVqDcCFWMW4l1JCXgCiXoinmsm9h_4teRnZBzeFEyfV0E_jcEhe4w_YHtmFNjiawtCnDUX2yXbuOvp11zFyjB33U7wU5y0dIq_-dynqu9u6ekg3z_eP1XqTUolFyorJKuexQcpLp7UjMs41FtgpVmjYF8603PjWM1tbojSSco9GtVAUXi_F9V_2OA4fM8dptx_msf9-3EEOxkqVI-gvCfpQsg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2624801652</pqid></control><display><type>article</type><title>Teaching Predictive Control Using Specification-based Summative Assessments</title><source>Publicly Available Content Database</source><creator>McInerney, Ian ; Kerrigan, Eric C</creator><creatorcontrib>McInerney, Ian ; Kerrigan, Eric C</creatorcontrib><description>Including Model Predictive Control (MPC) in the undergraduate/graduate control curriculum is becoming vitally important due to the growing adoption of MPC in many industrial areas. In this paper, we present an overview of the predictive control course taught by the authors at Imperial College London between 2018 and 2021. We discuss how the course evolved from focusing solely on the linear MPC formulation to covering nonlinear MPC and some of its extensions. We also present a novel specification-based summative assessment framework, written in MATLAB, that was developed to assess the knowledge and understanding of the students in the course by tasking them with designing a controller for a real-world problem. The MATLAB assessment framework was designed to provide the students with the freedom to design and implement any MPC controller they wanted. The submitted controllers were then assessed against over 30 variations of the real-world problem to gauge student understanding of design robustness and the MPC topics from the course.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2202.00157</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Colleges &amp; universities ; Control systems design ; Controllers ; Curricula ; Industrial areas ; Matlab ; Predictive control ; Specifications ; Students</subject><ispartof>arXiv.org, 2022-04</ispartof><rights>2022. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2624801652?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>776,780,25731,27902,36989,44566</link.rule.ids></links><search><creatorcontrib>McInerney, Ian</creatorcontrib><creatorcontrib>Kerrigan, Eric C</creatorcontrib><title>Teaching Predictive Control Using Specification-based Summative Assessments</title><title>arXiv.org</title><description>Including Model Predictive Control (MPC) in the undergraduate/graduate control curriculum is becoming vitally important due to the growing adoption of MPC in many industrial areas. In this paper, we present an overview of the predictive control course taught by the authors at Imperial College London between 2018 and 2021. We discuss how the course evolved from focusing solely on the linear MPC formulation to covering nonlinear MPC and some of its extensions. We also present a novel specification-based summative assessment framework, written in MATLAB, that was developed to assess the knowledge and understanding of the students in the course by tasking them with designing a controller for a real-world problem. The MATLAB assessment framework was designed to provide the students with the freedom to design and implement any MPC controller they wanted. The submitted controllers were then assessed against over 30 variations of the real-world problem to gauge student understanding of design robustness and the MPC topics from the course.</description><subject>Colleges &amp; universities</subject><subject>Control systems design</subject><subject>Controllers</subject><subject>Curricula</subject><subject>Industrial areas</subject><subject>Matlab</subject><subject>Predictive control</subject><subject>Specifications</subject><subject>Students</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNotjctqwzAQRUWh0JDmA7ozdG1XGmlseRlMXzTQQtx1GEvjViG2U8sO_fw-V3dxDucKcaVkZiyivKHxM5wyAAmZlAqLM7EArVVqDcCFWMW4l1JCXgCiXoinmsm9h_4teRnZBzeFEyfV0E_jcEhe4w_YHtmFNjiawtCnDUX2yXbuOvp11zFyjB33U7wU5y0dIq_-dynqu9u6ekg3z_eP1XqTUolFyorJKuexQcpLp7UjMs41FtgpVmjYF8603PjWM1tbojSSco9GtVAUXi_F9V_2OA4fM8dptx_msf9-3EEOxkqVI-gvCfpQsg</recordid><startdate>20220429</startdate><enddate>20220429</enddate><creator>McInerney, Ian</creator><creator>Kerrigan, Eric C</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20220429</creationdate><title>Teaching Predictive Control Using Specification-based Summative Assessments</title><author>McInerney, Ian ; Kerrigan, Eric C</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a957-e1ea81cd5b5a69c33caa4ccb82ec1e154ed7c4febdfdee8895040a6d541f277d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Colleges &amp; universities</topic><topic>Control systems design</topic><topic>Controllers</topic><topic>Curricula</topic><topic>Industrial areas</topic><topic>Matlab</topic><topic>Predictive control</topic><topic>Specifications</topic><topic>Students</topic><toplevel>online_resources</toplevel><creatorcontrib>McInerney, Ian</creatorcontrib><creatorcontrib>Kerrigan, Eric C</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McInerney, Ian</au><au>Kerrigan, Eric C</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Teaching Predictive Control Using Specification-based Summative Assessments</atitle><jtitle>arXiv.org</jtitle><date>2022-04-29</date><risdate>2022</risdate><eissn>2331-8422</eissn><abstract>Including Model Predictive Control (MPC) in the undergraduate/graduate control curriculum is becoming vitally important due to the growing adoption of MPC in many industrial areas. In this paper, we present an overview of the predictive control course taught by the authors at Imperial College London between 2018 and 2021. We discuss how the course evolved from focusing solely on the linear MPC formulation to covering nonlinear MPC and some of its extensions. We also present a novel specification-based summative assessment framework, written in MATLAB, that was developed to assess the knowledge and understanding of the students in the course by tasking them with designing a controller for a real-world problem. The MATLAB assessment framework was designed to provide the students with the freedom to design and implement any MPC controller they wanted. The submitted controllers were then assessed against over 30 variations of the real-world problem to gauge student understanding of design robustness and the MPC topics from the course.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2202.00157</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2022-04
issn 2331-8422
language eng
recordid cdi_proquest_journals_2624801652
source Publicly Available Content Database
subjects Colleges & universities
Control systems design
Controllers
Curricula
Industrial areas
Matlab
Predictive control
Specifications
Students
title Teaching Predictive Control Using Specification-based Summative Assessments
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-07T15%3A40%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Teaching%20Predictive%20Control%20Using%20Specification-based%20Summative%20Assessments&rft.jtitle=arXiv.org&rft.au=McInerney,%20Ian&rft.date=2022-04-29&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.2202.00157&rft_dat=%3Cproquest%3E2624801652%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-a957-e1ea81cd5b5a69c33caa4ccb82ec1e154ed7c4febdfdee8895040a6d541f277d3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2624801652&rft_id=info:pmid/&rfr_iscdi=true