An improved ellipsoid optimization algorithm in subspace predictive control
Purpose The purpose of this paper is to derive the output predictor for a stationary normal process with rational spectral density and linear stochastic discrete-time state-space model, respectively, as the output predictor is very important in model predictive control. The derivations are only depe...
Gespeichert in:
| Veröffentlicht in: | Aircraft engineering 2021-08, Vol.93 (5), p.870-879 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 879 |
|---|---|
| container_issue | 5 |
| container_start_page | 870 |
| container_title | Aircraft engineering |
| container_volume | 93 |
| creator | Jianhong, Wang |
| description | Purpose
The purpose of this paper is to derive the output predictor for a stationary normal process with rational spectral density and linear stochastic discrete-time state-space model, respectively, as the output predictor is very important in model predictive control. The derivations are only dependent on matrix operations. Based on the output predictor, one quadratic programming problem is constructed to achieve the goal of subspace predictive control. Then an improved ellipsoid optimization algorithm is proposed to solve the optimal control input and the complexity analysis of this improved ellipsoid optimization algorithm is also given to complete the previous work. Finally, by the example of the helicopter, the efficiency of the proposed control strategy can be easily realized.
Design/methodology/approach
First, a stationary normal process with rational spectral density and one stochastic discrete-time state-space model is described. Second, the output predictors for these two forms are derived, respectively, and the derivation processes are dependent on the Diophantine equation and some basic matrix operations. Third, after inserting these two output predictors into the cost function of predictive control, the control input can be solved by using the improved ellipsoid optimization algorithm and the complexity analysis corresponding to this improved ellipsoid optimization algorithm is also provided.
Findings
Subspace predictive control can not only enable automatically tune the parameters in predictive control but also avoids many steps in classical linear Gaussian control. It means that subspace predictive control is independent of any prior knowledge of the controller. An improved ellipsoid optimization algorithm is used to solve the optimal control input and the complexity analysis of this algorithm is also given.
Originality/value
To the best knowledge of the authors, this is the first attempt at deriving the output predictors for stationary normal processes with rational spectral density and one stochastic discrete-time state-space model. Then, the derivation processes are dependent on the Diophantine equation and some basic matrix operations. The complexity analysis corresponding to this improved ellipsoid optimization algorithm is analyzed. |
| doi_str_mv | 10.1108/AEAT-04-2019-0073 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_emera</sourceid><recordid>TN_cdi_proquest_journals_2558110852</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2558110852</sourcerecordid><originalsourceid>FETCH-LOGICAL-c244t-ae2b36161d434464c752a9c2347af6dd93a094c0a991fbaf37823b37c25fc86c3</originalsourceid><addsrcrecordid>eNptkL1OwzAURi0EEqXwAGyWmA3-TZwxqgpFVGIps-XYDrhK4mC7leDpSVQWJKZ7h-989-oAcEvwPSFYPtTreocwRxSTCmFcsjOwIKWQiFPCzuedSyQlp5fgKqU9xqQQmC3ASz1A348xHJ2Fruv8mIK3MIzZ9_5bZx8GqLv3EH3-6KEfYDo0adTGwTE66032RwdNGHIM3TW4aHWX3M3vXIK3x_VutUHb16fnVb1FhnKekXa0YQUpiOWM84KbUlBdGcp4qdvC2oppXHGDdVWRttEtKyVlDSsNFa2RhWFLcHfqnd7-PLiU1T4c4jCdVFQIOfsQdEqRU8rEkFJ0rRqj73X8UgSrOaNmZwpzNTtTs7OJwSfG9S7qzv6L_NHMfgBUyG3K</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2558110852</pqid></control><display><type>article</type><title>An improved ellipsoid optimization algorithm in subspace predictive control</title><source>Emerald</source><creator>Jianhong, Wang</creator><creatorcontrib>Jianhong, Wang</creatorcontrib><description>Purpose
The purpose of this paper is to derive the output predictor for a stationary normal process with rational spectral density and linear stochastic discrete-time state-space model, respectively, as the output predictor is very important in model predictive control. The derivations are only dependent on matrix operations. Based on the output predictor, one quadratic programming problem is constructed to achieve the goal of subspace predictive control. Then an improved ellipsoid optimization algorithm is proposed to solve the optimal control input and the complexity analysis of this improved ellipsoid optimization algorithm is also given to complete the previous work. Finally, by the example of the helicopter, the efficiency of the proposed control strategy can be easily realized.
Design/methodology/approach
First, a stationary normal process with rational spectral density and one stochastic discrete-time state-space model is described. Second, the output predictors for these two forms are derived, respectively, and the derivation processes are dependent on the Diophantine equation and some basic matrix operations. Third, after inserting these two output predictors into the cost function of predictive control, the control input can be solved by using the improved ellipsoid optimization algorithm and the complexity analysis corresponding to this improved ellipsoid optimization algorithm is also provided.
Findings
Subspace predictive control can not only enable automatically tune the parameters in predictive control but also avoids many steps in classical linear Gaussian control. It means that subspace predictive control is independent of any prior knowledge of the controller. An improved ellipsoid optimization algorithm is used to solve the optimal control input and the complexity analysis of this algorithm is also given.
Originality/value
To the best knowledge of the authors, this is the first attempt at deriving the output predictors for stationary normal processes with rational spectral density and one stochastic discrete-time state-space model. Then, the derivation processes are dependent on the Diophantine equation and some basic matrix operations. The complexity analysis corresponding to this improved ellipsoid optimization algorithm is analyzed.</description><identifier>ISSN: 1748-8842</identifier><identifier>EISSN: 1758-4213</identifier><identifier>DOI: 10.1108/AEAT-04-2019-0073</identifier><language>eng</language><publisher>Bradford: Emerald Publishing Limited</publisher><subject>Algorithms ; Complexity ; Control algorithms ; Control theory ; Controllers ; Cost function ; Density ; Derivation ; Design ; Diophantine equation ; Helicopter control ; Identification ; Mathematical models ; Noise control ; Optimal control ; Optimization ; Optimization algorithms ; Predictive control ; Quadratic programming ; Spectra ; State space models ; Subspaces ; System theory</subject><ispartof>Aircraft engineering, 2021-08, Vol.93 (5), p.870-879</ispartof><rights>Emerald Publishing Limited</rights><rights>Emerald Publishing Limited 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c244t-ae2b36161d434464c752a9c2347af6dd93a094c0a991fbaf37823b37c25fc86c3</citedby><cites>FETCH-LOGICAL-c244t-ae2b36161d434464c752a9c2347af6dd93a094c0a991fbaf37823b37c25fc86c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,967,27924,27925</link.rule.ids></links><search><creatorcontrib>Jianhong, Wang</creatorcontrib><title>An improved ellipsoid optimization algorithm in subspace predictive control</title><title>Aircraft engineering</title><description>Purpose
The purpose of this paper is to derive the output predictor for a stationary normal process with rational spectral density and linear stochastic discrete-time state-space model, respectively, as the output predictor is very important in model predictive control. The derivations are only dependent on matrix operations. Based on the output predictor, one quadratic programming problem is constructed to achieve the goal of subspace predictive control. Then an improved ellipsoid optimization algorithm is proposed to solve the optimal control input and the complexity analysis of this improved ellipsoid optimization algorithm is also given to complete the previous work. Finally, by the example of the helicopter, the efficiency of the proposed control strategy can be easily realized.
Design/methodology/approach
First, a stationary normal process with rational spectral density and one stochastic discrete-time state-space model is described. Second, the output predictors for these two forms are derived, respectively, and the derivation processes are dependent on the Diophantine equation and some basic matrix operations. Third, after inserting these two output predictors into the cost function of predictive control, the control input can be solved by using the improved ellipsoid optimization algorithm and the complexity analysis corresponding to this improved ellipsoid optimization algorithm is also provided.
Findings
Subspace predictive control can not only enable automatically tune the parameters in predictive control but also avoids many steps in classical linear Gaussian control. It means that subspace predictive control is independent of any prior knowledge of the controller. An improved ellipsoid optimization algorithm is used to solve the optimal control input and the complexity analysis of this algorithm is also given.
Originality/value
To the best knowledge of the authors, this is the first attempt at deriving the output predictors for stationary normal processes with rational spectral density and one stochastic discrete-time state-space model. Then, the derivation processes are dependent on the Diophantine equation and some basic matrix operations. The complexity analysis corresponding to this improved ellipsoid optimization algorithm is analyzed.</description><subject>Algorithms</subject><subject>Complexity</subject><subject>Control algorithms</subject><subject>Control theory</subject><subject>Controllers</subject><subject>Cost function</subject><subject>Density</subject><subject>Derivation</subject><subject>Design</subject><subject>Diophantine equation</subject><subject>Helicopter control</subject><subject>Identification</subject><subject>Mathematical models</subject><subject>Noise control</subject><subject>Optimal control</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Predictive control</subject><subject>Quadratic programming</subject><subject>Spectra</subject><subject>State space models</subject><subject>Subspaces</subject><subject>System theory</subject><issn>1748-8842</issn><issn>1758-4213</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNptkL1OwzAURi0EEqXwAGyWmA3-TZwxqgpFVGIps-XYDrhK4mC7leDpSVQWJKZ7h-989-oAcEvwPSFYPtTreocwRxSTCmFcsjOwIKWQiFPCzuedSyQlp5fgKqU9xqQQmC3ASz1A348xHJ2Fruv8mIK3MIzZ9_5bZx8GqLv3EH3-6KEfYDo0adTGwTE66032RwdNGHIM3TW4aHWX3M3vXIK3x_VutUHb16fnVb1FhnKekXa0YQUpiOWM84KbUlBdGcp4qdvC2oppXHGDdVWRttEtKyVlDSsNFa2RhWFLcHfqnd7-PLiU1T4c4jCdVFQIOfsQdEqRU8rEkFJ0rRqj73X8UgSrOaNmZwpzNTtTs7OJwSfG9S7qzv6L_NHMfgBUyG3K</recordid><startdate>20210805</startdate><enddate>20210805</enddate><creator>Jianhong, Wang</creator><general>Emerald Publishing Limited</general><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7RQ</scope><scope>7TB</scope><scope>7WY</scope><scope>7XB</scope><scope>8AF</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>K6~</scope><scope>KB.</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>M0F</scope><scope>M1Q</scope><scope>M2P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PDBOC</scope><scope>PQBIZ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20210805</creationdate><title>An improved ellipsoid optimization algorithm in subspace predictive control</title><author>Jianhong, Wang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c244t-ae2b36161d434464c752a9c2347af6dd93a094c0a991fbaf37823b37c25fc86c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Complexity</topic><topic>Control algorithms</topic><topic>Control theory</topic><topic>Controllers</topic><topic>Cost function</topic><topic>Density</topic><topic>Derivation</topic><topic>Design</topic><topic>Diophantine equation</topic><topic>Helicopter control</topic><topic>Identification</topic><topic>Mathematical models</topic><topic>Noise control</topic><topic>Optimal control</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Predictive control</topic><topic>Quadratic programming</topic><topic>Spectra</topic><topic>State space models</topic><topic>Subspaces</topic><topic>System theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jianhong, Wang</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Career and Technical Education</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ProQuest_ABI/INFORM Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>STEM Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection</collection><collection>Materials Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ABI/INFORM Trade & Industry</collection><collection>ProQuest Military Collection</collection><collection>ProQuest Science Journals</collection><collection>Engineering Database</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Materials science collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Aircraft engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jianhong, Wang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An improved ellipsoid optimization algorithm in subspace predictive control</atitle><jtitle>Aircraft engineering</jtitle><date>2021-08-05</date><risdate>2021</risdate><volume>93</volume><issue>5</issue><spage>870</spage><epage>879</epage><pages>870-879</pages><issn>1748-8842</issn><eissn>1758-4213</eissn><abstract>Purpose
The purpose of this paper is to derive the output predictor for a stationary normal process with rational spectral density and linear stochastic discrete-time state-space model, respectively, as the output predictor is very important in model predictive control. The derivations are only dependent on matrix operations. Based on the output predictor, one quadratic programming problem is constructed to achieve the goal of subspace predictive control. Then an improved ellipsoid optimization algorithm is proposed to solve the optimal control input and the complexity analysis of this improved ellipsoid optimization algorithm is also given to complete the previous work. Finally, by the example of the helicopter, the efficiency of the proposed control strategy can be easily realized.
Design/methodology/approach
First, a stationary normal process with rational spectral density and one stochastic discrete-time state-space model is described. Second, the output predictors for these two forms are derived, respectively, and the derivation processes are dependent on the Diophantine equation and some basic matrix operations. Third, after inserting these two output predictors into the cost function of predictive control, the control input can be solved by using the improved ellipsoid optimization algorithm and the complexity analysis corresponding to this improved ellipsoid optimization algorithm is also provided.
Findings
Subspace predictive control can not only enable automatically tune the parameters in predictive control but also avoids many steps in classical linear Gaussian control. It means that subspace predictive control is independent of any prior knowledge of the controller. An improved ellipsoid optimization algorithm is used to solve the optimal control input and the complexity analysis of this algorithm is also given.
Originality/value
To the best knowledge of the authors, this is the first attempt at deriving the output predictors for stationary normal processes with rational spectral density and one stochastic discrete-time state-space model. Then, the derivation processes are dependent on the Diophantine equation and some basic matrix operations. The complexity analysis corresponding to this improved ellipsoid optimization algorithm is analyzed.</abstract><cop>Bradford</cop><pub>Emerald Publishing Limited</pub><doi>10.1108/AEAT-04-2019-0073</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1748-8842 |
| ispartof | Aircraft engineering, 2021-08, Vol.93 (5), p.870-879 |
| issn | 1748-8842 1758-4213 |
| language | eng |
| recordid | cdi_proquest_journals_2558110852 |
| source | Emerald |
| subjects | Algorithms Complexity Control algorithms Control theory Controllers Cost function Density Derivation Design Diophantine equation Helicopter control Identification Mathematical models Noise control Optimal control Optimization Optimization algorithms Predictive control Quadratic programming Spectra State space models Subspaces System theory |
| title | An improved ellipsoid optimization algorithm in subspace predictive control |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T09%3A50%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_emera&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20improved%20ellipsoid%20optimization%20algorithm%20in%20subspace%20predictive%20control&rft.jtitle=Aircraft%20engineering&rft.au=Jianhong,%20Wang&rft.date=2021-08-05&rft.volume=93&rft.issue=5&rft.spage=870&rft.epage=879&rft.pages=870-879&rft.issn=1748-8842&rft.eissn=1758-4213&rft_id=info:doi/10.1108/AEAT-04-2019-0073&rft_dat=%3Cproquest_emera%3E2558110852%3C/proquest_emera%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2558110852&rft_id=info:pmid/&rfr_iscdi=true |