Efficient Model Predictive Algorithms for Tracking of Periodic Signals
This 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...
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| Vydáno v: | Journal of control science and engineering Ročník 2012; číslo 2012; s. 1 - 13 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Cairo, Egypt
Hindawi Puplishing Corporation
01.01.2012
Hindawi Publishing Corporation Wiley |
| ISSN: | 1687-5249, 1687-5257 |
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| Abstract | This 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. |
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| AbstractList | This 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. |
| Author | Chu, Yun-Chung Chen, Michael Z. Q. |
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| CitedBy_id | crossref_primary_10_1080_00207179_2014_953589 crossref_primary_10_1109_TEC_2013_2260162 crossref_primary_10_1155_2014_340842 crossref_primary_10_1155_2019_7973591 |
| Cites_doi | 10.1016/S0005-1098(01)00174-1 10.1023/A:1021711402723 10.1109/MCS.2011.940571 10.1109/9.241565 10.1023/A:1023696221899 10.1109/TAC.2003.816984 10.1109/TAC.2005.858679 10.1109/TII.2011.2106451 10.1137/0315033 10.1109/TIE.2009.2020705 10.1109/TAC.1976.1101137 10.1080/00207170310001643221 10.1016/0005-1098(76)90006-6 10.1016/S0967-0661(02)00186-7 10.1016/S0005-1098(02)00174-7 10.1016/S0959-1524(02)00003-3 |
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| Copyright | Copyright © 2012 Yun-Chung Chu and Michael Z. Q. Chen. |
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| Title | Efficient Model Predictive Algorithms for Tracking of Periodic Signals |
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