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Article: Efficient model predictive algorithms for tracking of periodic signals

TitleEfficient model predictive algorithms for tracking of periodic signals
Authors
KeywordsComputational requirements
Control inputs
Convergence properties
Dynamic policy
Fast-sampling systems
Issue Date2012
PublisherHindawi Publishing Corporation. The Journal's web site is located at http://www.hindawi.com/journals/jcse/
Citation
Journal of Control Science and Engineering, 2012, v. 2012, article no. 729748 How to Cite?
AbstractThis paper studies the design of efficient model predictive controllers for fast-sampling linear time-invariant systems subject to input constraints to track a set of periodic references. The problem is decomposed into a steady-state subproblem that determines the optimal asymptotic operating point and a transient subproblem that drives the given plant to this operating point. While the transient subproblem is a small-sized quadratic program, the steady-state subproblem can easily involve hundreds of variables and constraints. The decomposition allows these two subproblems of very different computational complexities to be solved in parallel with different sampling rates. Moreover, a receding horizon approach is adopted for the steady-state subproblem to spread the optimization over time in an efficient manner, making its solution possible for fast-sampling systems. Besides the conventional formulation based on the control inputs as variables, a parameterization using a dynamic policy on the inputs is introduced, which further reduces the online computational requirements. Both proposed algorithms possess nice convergence properties, which are also verified with computer simulations. Copyright © 2012 Yun-Chung Chu and Michael Z. Q. Chen.
Persistent Identifierhttp://hdl.handle.net/10722/159574
ISSN
2015 SCImago Journal Rankings: 0.299

 

DC FieldValueLanguage
dc.contributor.authorChu, YCen_US
dc.contributor.authorChen, MZQen_US
dc.date.accessioned2012-08-16T05:52:41Z-
dc.date.available2012-08-16T05:52:41Z-
dc.date.issued2012en_US
dc.identifier.citationJournal of Control Science and Engineering, 2012, v. 2012, article no. 729748en_US
dc.identifier.issn1687-5249-
dc.identifier.urihttp://hdl.handle.net/10722/159574-
dc.description.abstractThis paper studies the design of efficient model predictive controllers for fast-sampling linear time-invariant systems subject to input constraints to track a set of periodic references. The problem is decomposed into a steady-state subproblem that determines the optimal asymptotic operating point and a transient subproblem that drives the given plant to this operating point. While the transient subproblem is a small-sized quadratic program, the steady-state subproblem can easily involve hundreds of variables and constraints. The decomposition allows these two subproblems of very different computational complexities to be solved in parallel with different sampling rates. Moreover, a receding horizon approach is adopted for the steady-state subproblem to spread the optimization over time in an efficient manner, making its solution possible for fast-sampling systems. Besides the conventional formulation based on the control inputs as variables, a parameterization using a dynamic policy on the inputs is introduced, which further reduces the online computational requirements. Both proposed algorithms possess nice convergence properties, which are also verified with computer simulations. Copyright © 2012 Yun-Chung Chu and Michael Z. Q. Chen.-
dc.languageengen_US
dc.publisherHindawi Publishing Corporation. The Journal's web site is located at http://www.hindawi.com/journals/jcse/-
dc.relation.ispartofJournal of Control Science and Engineeringen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectComputational requirements-
dc.subjectControl inputs-
dc.subjectConvergence properties-
dc.subjectDynamic policy-
dc.subjectFast-sampling systems-
dc.titleEfficient model predictive algorithms for tracking of periodic signalsen_US
dc.typeArticleen_US
dc.identifier.emailChu, YC: ycchu92@hku.hken_US
dc.identifier.emailChen, MZQ: mzqchen@hku.hken_US
dc.identifier.authorityChen, MZQ=rp01317en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1155/2012/729748-
dc.identifier.scopuseid_2-s2.0-84863056684-
dc.identifier.hkuros205201en_US
dc.identifier.volume2012, article no. 729748en_US
dc.publisher.placeUnited States-

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