Dynamical system identification with the Generalized Laguerre functions

Tuma, Martin; Jura, Pavel

doi:10.1109/icumt.2015.7382431

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Initially, in dynamic systems theory, the Laguerre orthogonal functions have been used for system identification, because these functions could improve the numerical accuracy of the corresponding linear regression estimation problem 21‐24 . Furthermore, a Laguerre model is produced by the discrete‐time impulse response of a dynamic system and the same discrete‐time impulse response that leads to the LDMPC design using Laguerre functions as shown by Reference 25.…”

Section: Laguerre Dmpc Designmentioning

confidence: 99%

Constrained discrete model predictive control of an arm‐manipulator using Laguerre function

Pinheiro

Silveira

2020

Optim Control Appl Methods

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SummaryThis work presents a multivariable predictive controller applied on a redundant robotic manipulator with three degrees of freedom. The article focuses on the design of a discrete model‐based predictive controller (DMPC) using the Laguerre function as a control effort weighting method to enhance the solution of Hildreth's quadratic programming and to minimize the trade‐off problem in constrained case. The Laguerre functions are used to simplify and enhance the control horizon effect through parsimonious control trajectory, thus reducing the computational load required to find the optimal control solution. Furthermore, these results can be confirmed by simulations and experimental tests on the manipulator and comparing it to the traditional DMPC approach and the discrete linear quadratic regulator.

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Section: Laguerre Dmpc Designmentioning

confidence: 99%

Constrained discrete model predictive control of an arm‐manipulator using Laguerre function

Pinheiro

Silveira

2020

Optim Control Appl Methods

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“…The applications of GLFs can be found in the field of theoretical mathematics, see [19], [20], but these functions definitely deserve more attention in the control theory field. The identification method based on GLFs was introduced in [21]. This method was further compared with least squares based identification with state variable filters (LSSVF) in [22].…”

Section: Introductionmentioning

confidence: 99%

Design PID Controllers Using Generalized Laguerre Functions

Karský¹,

Tůma²

2022

RADIOENGINEERING

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This paper deals with a method of designing PID controllers. Generalized Laguerre functions were used for this task. Generalized Laguerre functions generate an orthogonal base in the time domain and the operator domain. This property of generalized Laguerre functions is beneficially used for the design of the PID controller. Parameters for generalized Laguerre function PID controllers are computed from the Laguerre series of the open loop and the Laguerre series of the ideal open loop. To satisfy this goal, the plant transfer function, the controller transfer function, and the ideal open loop transfer function are transformed into a generalized Laguerre functions base. Three examples are shown to present this method.

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“…We note that the Laguerre model has received interest in the literature of many fields, such as Tanguy et al (2000), Aoun et al (2007),Wahlberg and Mkil (1996), Tuma and Jura (2015) and Wang and Jiang (2011) for the system identification; Asad and Hasan (2012), Mahmoodi et al (2009), Wang (2004), Yakub and Mori (2014) and Abdullah and Idres (2014) for control system; Horng and Chou (1988), Sachinn et al (2005), Ding et al (1990), Anfinsen and Aamao (2015) and King and Paraskevopoulos (1977) for system diagnosis; Chou and Horng (1986) for state estimation; Masnadi-Shirazi and Aleshams (2003) for filter design. In what follows, we assume that model of the system is known and we want to reduce complexity by expressing it as Laguerre filters.…”

Section: Introductionmentioning

confidence: 99%

Reduced model of linear systems via Laguerre filters

Anes

Bouzrara

Ragot

2017

Transactions of the Institute of Measurement and Control

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In this paper, we propose a technique to reduce the complexity of an existing (initial) model via the Laguerre filters. We present an analytical method for the parameter identification of the Fourier coefficients of the Laguerre model. This technique is based on the bilinear discrete transformation and in which the Fourier coefficients are expressed in recurrent form in terms of the Laguerre pole. This latter is estimated by an iterative technique, based on the Newton algorithm. This identification technique is after extended to the case of the ARX-Laguerre model and the MISO-ARX-Laguerre model and its performances are illustrated by numerical simulations.

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Dynamical system identification with the Generalized Laguerre functions

Cited by 6 publications

References 13 publications

Constrained discrete model predictive control of an arm‐manipulator using Laguerre function

Constrained discrete model predictive control of an arm‐manipulator using Laguerre function

Design PID Controllers Using Generalized Laguerre Functions

Reduced model of linear systems via Laguerre filters

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