Interpolating model identification for SISO linear parameter-varying systems
This paper presents a new method to estimate linear parameter-varying (LPV) state-space models for single-input single-output systems whose dynamics depend on one or more time-varying parameters, called scheduling parameters. The method is based on the interpolation of linear time-invariant models t...
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| Published in: | Mechanical systems and signal processing Vol. 23; no. 8; pp. 2395 - 2417 |
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| Abstract | This paper presents a new method to estimate linear parameter-varying (LPV) state-space models for single-input single-output systems whose dynamics depend on one or more time-varying parameters, called scheduling parameters. The method is based on the interpolation of linear time-invariant models that are identified for fixed operating conditions of the system, that is, for constant values of the scheduling parameters. The proposed method can account for multiple scheduling parameters and yields either a polynomial or an affine LPV model that is numerically well-conditioned and therefore suitable for LPV control synthesis. The underlying interpolation technique is formulated as a nonlinear least-squares optimization problem that can be solved efficiently by standard solvers. The new interpolation method is applied to an electromechanical system that depends on two scheduling parameters. The numerical results are compared to existing techniques in the literature, demonstrating the potential and advantages of the proposed method. |
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| AbstractList | This paper presents a new method to estimate linear parameter-varying (LPV) state-space models for single-input single-output systems whose dynamics depend on one or more time-varying parameters, called scheduling parameters. The method is based on the interpolation of linear time-invariant models that are identified for fixed operating conditions of the system, that is, for constant values of the scheduling parameters. The proposed method can account for multiple scheduling parameters and yields either a polynomial or an affine LPV model that is numerically well-conditioned and therefore suitable for LPV control synthesis. The underlying interpolation technique is formulated as a nonlinear least-squares optimization problem that can be solved efficiently by standard solvers. The new interpolation method is applied to an electromechanical system that depends on two scheduling parameters. The numerical results are compared to existing techniques in the literature, demonstrating the potential and advantages of the proposed method. |
| Author | Swevers, Jan De Caigny, Jan Camino, Juan F. |
| Author_xml | – sequence: 1 givenname: Jan surname: De Caigny fullname: De Caigny, Jan organization: Department of Mechanical Engineering, Katholieke Universiteit Leuven, 300B, B-3001 Heverlee, Belgium – sequence: 2 givenname: Juan F. surname: Camino fullname: Camino, Juan F. email: camino@fem.unicamp.br organization: School of Mechanical Engineering, University of Campinas - UNICAMP, P.O. Box 6122, 13083-970 Campinas, SP, Brazil – sequence: 3 givenname: Jan surname: Swevers fullname: Swevers, Jan organization: Department of Mechanical Engineering, Katholieke Universiteit Leuven, 300B, B-3001 Heverlee, Belgium |
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| Keywords | Linear parameter-varying systems State-space model interpolation System identification Linear interpolation Least squares problem Control synthesis Linear parameter varying system State space Scheduling Non linear programming State space method Linear control Modeling Operating conditions Time varying system Linear time invariant system Model matching Well conditioned SISO system Electromechanical system |
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| Snippet | This paper presents a new method to estimate linear parameter-varying (LPV) state-space models for single-input single-output systems whose dynamics depend on... |
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| SubjectTerms | Applied sciences Computer science; control theory; systems Control system synthesis Control theory. Systems Exact sciences and technology Fundamental areas of phenomenology (including applications) Linear parameter-varying systems Measurement and testing methods Modelling and identification Physics Solid mechanics State-space model interpolation Structural and continuum mechanics System identification |
| Title | Interpolating model identification for SISO linear parameter-varying systems |
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