Quantised MPC for LPV systems by using new Lyapunov–Krasovskii functional
This study deals with the problem of sampled-data model predictive control (MPC) for linear parameter varying (LPV) systems with input quantisation. The LPV systems under consideration depend on a set of parameters that are bounded and available online. To deal with a piecewise constant sampled-data...
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| Published in | IET control theory & applications Vol. 11; no. 3; pp. 439 - 445 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
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The Institution of Engineering and Technology
03.02.2017
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| Abstract | This study deals with the problem of sampled-data model predictive control (MPC) for linear parameter varying (LPV) systems with input quantisation. The LPV systems under consideration depend on a set of parameters that are bounded and available online. To deal with a piecewise constant sampled-data and quantisation of the control input, the closed-loop system is modelled as a continuous-time impulsive dynamic model with sector non-linearity. The control problem is formulated as a minimisation of the upper bound of infinite horizon cost function subject to a sufficient condition for stability. The stability of the proposed MPC is guaranteed by constructing new Lyapunov–Krasovskii functional. Finally, a numerical example is provided to illustrate the effectiveness and benefits of the proposed theoretical results. |
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| AbstractList | This study deals with the problem of sampled‐data model predictive control (MPC) for linear parameter varying (LPV) systems with input quantisation. The LPV systems under consideration depend on a set of parameters that are bounded and available online. To deal with a piecewise constant sampled‐data and quantisation of the control input, the closed‐loop system is modelled as a continuous‐time impulsive dynamic model with sector non‐linearity. The control problem is formulated as a minimisation of the upper bound of infinite horizon cost function subject to a sufficient condition for stability. The stability of the proposed MPC is guaranteed by constructing new Lyapunov–Krasovskii functional. Finally, a numerical example is provided to illustrate the effectiveness and benefits of the proposed theoretical results. |
| Author | Lee, Sangmoon Kwon, Ohmin |
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| CitedBy_id | crossref_primary_10_1016_j_nahs_2018_08_003 crossref_primary_10_1049_iet_cta_2017_0980 crossref_primary_10_1016_j_amc_2021_126473 crossref_primary_10_1002_rnc_5454 crossref_primary_10_1002_rnc_4043 |
| Cites_doi | 10.1109/TAC.2009.2037464 10.1016/j.automatica.2013.02.033 10.1016/0005-1098(96)00063-5 10.1049/iet-cta:20060525 10.1049/iet-cta.2014.0205 10.1016/j.automatica.2015.03.016 10.1016/S0098-1354(98)00301-9 10.1109/ACC.2008.4586641 10.1016/S0005-1098(99)00176-4 10.1109/TAC.2011.2122610 10.1109/9.486646 10.1109/TAC.2010.2074590 10.1016/j.automatica.2013.05.030 10.1016/S0005-1098(00)00156-4 10.1007/978-1-4614-1833-7 10.1016/S0005-1098(99)00214-9 10.1016/j.automatica.2012.03.026 10.1137/1.9781611970777 |
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| Copyright | The Institution of Engineering and Technology 2021 The Authors. IET Control Theory & Applications published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology |
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| Keywords | sampled-data model predictive control control input quantisation closed loop systems infinite horizon cost function sampled data systems upper bound minimisation sector nonlinearity linear parameter varying systems control nonlinearities Lyapunov-Krasovskii functional continuous-time impulsive dynamic model LPV systems continuous time systems predictive control quantised MPC closed-loop system piecewise constant sampled-data infinite horizon |
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| SubjectTerms | closed loop systems closed‐loop system Constants Construction costs continuous time systems continuous‐time impulsive dynamic model control input quantisation control nonlinearities Control systems Dynamic models Dynamical systems infinite horizon infinite horizon cost function linear parameter varying systems LPV systems Lyapunov‐Krasovskii functional Mathematical models Nonlinear dynamics Parameters piecewise constant sampled‐data predictive control quantised MPC Research Article sampled data systems sampled‐data model predictive control sector nonlinearity upper bound minimisation |
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| Title | Quantised MPC for LPV systems by using new Lyapunov–Krasovskii functional |
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