Tracking setpoint robust model predictive control for input saturated and softened state constraints
This paper starts with a brief review of robust model predictive control (RMPC) schemes for uncertain systems using linear matrix inequalities (LMIs) subject to input saturated and softened state constraints. However when RMPC has both input and state constraints, difficulties will arise due to the...
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| Published in | International journal of control, automation, and systems Vol. 9; no. 5; pp. 958 - 965 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Heidelberg
Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
01.10.2011
Springer Nature B.V 제어·로봇·시스템학회 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1598-6446 2005-4092 |
| DOI | 10.1007/s12555-011-0517-4 |
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| Abstract | This paper starts with a brief review of robust model predictive control (RMPC) schemes for uncertain systems using linear matrix inequalities (LMIs) subject to input saturated and softened state constraints. However when RMPC has both input and state constraints, difficulties will arise due to the inability to satisfy the state constraints. In this paper, we develop two new tracking setpoint RMPC schemes with common Lyapunov function and with zero terminal equality subject to input saturated and softened state constraints. A brief comparative simulation of the two new RMPC schemes is implemented via examples to demonstrate the ability of the new RMPC schemes. |
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| AbstractList | This paper starts with a brief review of robust model predictive control (RMPC) schemes for uncertain systems using linear matrix inequalities (LMIs) subject to input saturated and softened state constraints. However when RMPC has both input and state constraints, difficulties will arise due to the inability to satisfy the state constraints. In this paper, we develop two new tracking setpoint RMPC schemes with common Lyapunov function and with zero terminal equality subject to input saturated and softened state constraints. A brief comparative simulation of the two new RMPC schemes is implemented via examples to demonstrate the ability of the new RMPC schemes. This paper starts with a brief review of robust model predictive control (RMPC) schemes for uncertain systems using linear matrix inequalities (LMIs) subject to input saturated and softened state constraints. However when RMPC has both input and state constraints, difficulties will arise due to the inability to satisfy the state constraints. In this paper, we develop two new tracking setpoint RMPC schemes with common Lyapunov function and with zero terminal equality subject to input saturated and softened state constraints. A brief comparative simulation of the two new RMPC schemes is implemented via examples to demonstrate the ability of the new RMPC schemes. KCI Citation Count: 9 This paper starts with a brief review of robust model predictive control (RMPC) schemes for uncertain systems using linear matrix inequalities (LMIs) subject to input saturated and softened state constraints. However when RMPC has both input and state constraints, difficulties will arise due to the inability to satisfy the state constraints. In this paper, we develop two new tracking setpoint RMPC schemes with common Lyapunov function and with zero terminal equality subject to input saturated and softened state constraints. A brief comparative simulation of the two new RMPC schemes is implemented via examples to demonstrate the ability of the new RMPC schemes.[PUBLICATION ABSTRACT] |
| Author | Minh, Vu Trieu Hashim, Fakhruldin Bin Mohd |
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| CitedBy_id | crossref_primary_10_3390_electronics12020445 crossref_primary_10_1007_s12555_012_0006_4 crossref_primary_10_3390_app112210668 crossref_primary_10_1007_s12555_012_0377_6 crossref_primary_10_1007_s40313_016_0231_9 crossref_primary_10_1109_TCSI_2023_3264207 crossref_primary_10_1002_asjc_1565 crossref_primary_10_1515_eng_2017_0012 crossref_primary_10_1002_rnc_3464 crossref_primary_10_3390_app112311143 crossref_primary_10_3390_electronics10212593 crossref_primary_10_1080_00207721_2017_1280551 crossref_primary_10_3390_en14217256 |
| Cites_doi | 10.1016/S0005-1098(00)00166-7 10.23919/ACC.1993.4792879 10.1111/j.1934-6093.2006.tb00284.x 10.1016/S0005-1098(99)00112-0 10.1109/9.863597 10.1007/978-94-011-5094-1_16 10.1016/S0005-1098(00)00170-9 10.1007/BF00938540 10.1002/asjc.162 10.1016/j.automatica.2006.10.001 10.1016/S0098-1354(98)00301-9 10.1007/978-1-4471-0853-5_19 10.1109/37.845037 10.1016/0005-1098(96)00063-5 10.1109/9.704989 10.1007/s12555-011-0306-0 10.1016/S0005-1098(01)00036-X 10.1007/s12555-011-0118-2 |
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| Keywords | robust model predictive control uncertain systems tracking setpoints Min-max control law softened state constraints |
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| References_xml | – reference: DingB.Properties of parameter-dependent openloop MPC for uncertain systems with politopic descriptionAsian Journal of Control201012158702676771 – reference: MinhV. T.AwangM.ParmanS.Conditions for stabilizability of linear switched systemsInternational Journal of Control, Automation, and Systems20119113914410.1007/s12555-011-0118-2 – reference: RawlingsJ.Tutorial overview of model predictive controlIEEE Contr. Syst. Mag.20002033862157346110.1109/37.845037 – reference: KeerthiS.GilbertE.Optimal infinite horizon feedback laws for a general class of constrained discrete time systems: stability and moving horizon approximationsJ. Optim. Theory Appl.199857226529393887510.1007/BF00938540 – reference: F. Allgower, et al., “Nonlinear predictive control and moving horizon estimation — an introductory overview,” In Advances in Control: Cont. Conf., London, pp. 391–449, 1999. – reference: MinhV. T.AfzulpurkarN.A comparative study on computational schemes for nonlinear model predictive controlAsian Journal of Control200684324331238298510.1111/j.1934-6093.2006.tb00284.x – reference: MagniL.NijmeijerH.van de SchaftA.A receding-horizon approach to the nonlinear h∞ control problemAutomatica200137342943518439940976.9301810.1016/S0005-1098(00)00166-7 – reference: KothareM.BalakrishmanV.MorariM.Robust constrained model predictive control using linear matrix inequalitiesAutomatica199632136113790897.9302310.1016/0005-1098(96)00063-5 – reference: PrimbesJ.The analysis of optimization based on controllersAutomatica2001376933938183460310.1016/S0005-1098(01)00036-X – reference: MinhV. T.NitinA.MohamadW.Fault detection and control of process systemMathematical Problem in Engineering2007200720 – reference: CasavolaA.MoscaE.A correction to minmax predictive control strategies for input-saturated polytopic uncertain systemsAutomatica200743238438522818451110.9302410.1016/j.automatica.2006.10.001 – reference: MorariM.LeeJ.Model predictive control: past, present and futureComput. Chem. Eng.1999234–566768210.1016/S0098-1354(98)00301-9 – reference: MayneD.MichalskaH.Robust receeding horizon control of constrained nonlinear systemsProc. IEEE.1993381623163312500340790.93038 – reference: ScokaertP.MayneD.Min-max feedback model predictive control for constrained linear systemsIEEE Trans. Automat. Contr.19984381136114216364670957.9303410.1109/9.704989 – reference: HenrionD.ArzelierD.PeaucelleD.SebekM.An LMI condition for robust stability of polynomial matrix polytopesAutomatica200137346146818439900982.9305710.1016/S0005-1098(00)00170-9 – reference: RamiM.ZhouX.Linear matrix inequalities, Riccati equations, and indefinite stochastic linear quadratic controlsIEEE Trans. Automat. Contr.20004561131114317783710981.9308010.1109/9.863597 – reference: CasavolaA.GiannelliM.MoscaE.Minmax predictive control strategies for input-saturated polytopic uncertain systemsAutomatica200036112513318336800939.9350610.1016/S0005-1098(99)00112-0 – reference: MinhV. T.HashimF.Time forward observer based adaptive controller for a teleoperation systemInternational Journal of Control, Automation, and Systems20119347047710.1007/s12555-011-0306-0 – reference: MinhV. T.AfzulpurkarN.Robust model predictive control for input saturated and softened state constraintsAsian Journal of Control200573323329 – reference: Q. Zheng and M. Morari, “Robust stability of constrained model predictive control,” Proc. Amer. Contr. Confr., San Francisco, CA, U.S.A, pp. 379–383, 1993. – reference: H. Chen and F. Allgower, Nonlinear Model Predictive Control Schemes with Guaranteed Stability, Kluwer Academic Publisher, U.S.A., pp. 456–494, 1998. – volume: 37 start-page: 429 issue: 3 year: 2001 ident: 517_CR2 publication-title: Automatica doi: 10.1016/S0005-1098(00)00166-7 – volume: 38 start-page: 1623 year: 1993 ident: 517_CR5 publication-title: Proc. 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Contr. doi: 10.1109/9.704989 – volume: 9 start-page: 470 issue: 3 year: 2011 ident: 517_CR20 publication-title: International Journal of Control, Automation, and Systems doi: 10.1007/s12555-011-0306-0 – volume: 37 start-page: 933 issue: 6 year: 2001 ident: 517_CR13 publication-title: Automatica doi: 10.1016/S0005-1098(01)00036-X – volume: 9 start-page: 139 issue: 1 year: 2011 ident: 517_CR21 publication-title: International Journal of Control, Automation, and Systems doi: 10.1007/s12555-011-0118-2 |
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| SubjectTerms | Comparative analysis Control Control systems Engineering Equality Linear matrix inequalities Lyapunov functions Mathematical models Mechatronics Predictive control Robotics Simulation Studies Systems stability Technical Notes and Correspondence Terminals Tracking 제어계측공학 |
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| Title | Tracking setpoint robust model predictive control for input saturated and softened state constraints |
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| ispartofPNX | International Journal of Control, 2011, Automation, and Systems, 9(5), , pp.958-965 |
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