Optimisation-based modelling of LPV systems using an -objective

Petersson, Daniel; Löfberg, Johan

doi:10.1080/00207179.2013.878477

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…Hence, solving (21) is not based on any, additional training data from motor experiments but solely focused on the previously identified system matrices in M i , i.e., a static optimization problem. The matrix norm in (21) can be any reasonable choice such as the Frobenius norm [99], the H 2 norm [100] or the H ∞ norm [101].…”

Section: Global-lpv System Excitationmentioning

confidence: 99%

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

Wallscheid

2021

IEEE Open J. Ind. Applicat.

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Temperature sensing in electric motors is an important task to ensure component protection against excessive heat while maximizing the power and torque capabilities. In order to optimize the dynamic performance limits of a motor during online operation, important motor temperatures must be known in real time. Since temperature measurements are associated with costs and integration efforts, model-based estimation methods became highly relevant. In recent years, many promising contributions have been made to this field leading to a vast literature basis. This paper organizes the literary landscape and gives a bird's-eye overview of the three most important estimation classes: Indirect methods, which track temperature-sensitive electrical motor parameters, and direct methods, namely lumped-parameter thermal networks as well as supervised machine learning. Practical limitations as well as special challenges of the respective methods are emphasized throughout the text. The aim of the paper is therefore to facilitate the entry for interested researchers and engineers in this field as well as to stimulate new research impulses. Hence, open problems as well as future research questions are addressed at the end of this review.

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Section: Global-lpv System Excitationmentioning

confidence: 99%

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

Wallscheid

2021

IEEE Open J. Ind. Applicat.

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“…As explained first in [22,23], then in [36,35], the standard interpolation step involved in the local approach can be efficiently discarded by optimizing one global cost-function which fits the local behavior of the sought global LPV model (with coherent local and global state basis) to the available local information available in the local LTI models estimated during the second step of the local approach. However, contrary to the developments available in [21,24], we do not consider an H 2 -normbased optimization solution hereafter but an H ∞ -norm-based one. The main reason why we focus on this specific norm is linked to the fact that the H ∞ -norm is a direct and efficient tool for adapting reliable and convergent non-smooth optimization algorithms [1, 2, 3] (suggested initially for H ∞ -synthesis) for parameterized model identification.…”

Section: Introductionmentioning

confidence: 99%

An H∞-norm-based approach for operating point selection and LPV model identification from local experiments

Vizer

Mercère

2014

Period. Polytech. Elec. Eng. Comp. Sci.

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When the identification of linear parameter-varying (LPV) models from local experiments is considered, the question of the necessary number of local operating points as well as the problem of the efficient interpolation of the locally-estimated linear Keywords linear parameter-varying model · linear fractional representation · H ∞ -norm · non-smooth optimization · nu-gap metric IntroductionNowadays, the extensive demand for reliable models of nonlinear and/or time-varying systems requires the development of special model structures in which the non-linear behavior is broken down into several local models [16]. Among all the multi-model structures available in the literature, a particular attention has been paid to the linear parameter-varying (LPV) models during the last two decades (see [28, Chapter 1] for a historical review of LPV modeling and identification). This interest can be mainly explained by the following reasons. First, an LPV model can be seen as a combination of local models with parameters evolving as a function of measurable variables (called the scheduling variables) which can be related to the different operating points of the system. By this way, the model structure is close to the standard linear time-invariant (LTI) one but with a structural flexibility able to cope with time-varying, even non-linear behaviors. Second, the development of LPV models is linked to control engineering, where a controller must be designed in order to guarantee a suitable closed-loop performance for a given plant in different operating conditions. A well-known example of controller design technique using this "divide and conquer" basic idea is the gain scheduling approach [25]. A wide body of LPV controller design techniques is now available for this problem, which can be solved reliably, provided that a suitable model in a parameter-dependent form has been derived. LPV model identification methods based on I/O data [7]can be divided into two sub-classes generally called the global approach and the local approach, respectively. On the one hand, the global approach assumes that one global experiment can be performed during which the control inputs, as well as the scheduling variables, are both excited (see among others e.g., [15,5,32,14,31] and the references therein). By this way, all the nonlinearities of the system are excited at the same time by passing through a large number of operating points. On the other hand, local methods are based on a multi-step procedure where• local experiments are performed in which the operating points (corresponding to fixed values of the scheduling

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A method for reducing implementation complexity in linear parameter-varying controllers

Bianchi

Peña

2022

Automatica

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Optimisation-based modelling of LPV systems using an -objective

Cited by 6 publications

References 27 publications

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

An H∞-norm-based approach for operating point selection and LPV model identification from local experiments

A method for reducing implementation complexity in linear parameter-varying controllers

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