Nonlinear model predictive control of a magnetic levitation system

Bächle, T.; Hentzelt, Sebastian; Graichen, Knut

doi:10.1016/j.conengprac.2013.04.009

Cited by 99 publications

(40 citation statements)

References 35 publications

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“…Los sistemas de levitación magnética en los últimos años se han considerado como un banco de pruebas para diversas estrategias de control (Lei, 2008), (Golob, 2003), (Qin, 2014), (Bächle, 2013) y (Beltran, 2015. Además, se han popularizado por las diversas aplicaciones que tienen, tales como: cojinetes magnéticos (Chen, 2010) y (Du, 2010); sistemas para trenes de levitación magnética (Hasirci, 2011); aislamiento de vibraciones (Tsuda, 2009); microrobots magnéticos (Kummer, 2010); máquinas eléctricas (Arrendondo, 2008); sistema de transportación magnética (Wai, 2011); sistemas de posicionamiento nanométrico (Kim, 2007), entre muchas otras (Peijnenburg, 2006), (Kimman, 2010), (Lee, 2006).…”

Section: Introductionunclassified

Modelado y control de un sistema de levitación magnética basado en un cojinete magnético activo

Cruz-Pegueros

Frías

Lozada‐Castillo

et al. 2017

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En este trabajo se presenta el diseño, modelado e implementación de un sistema de levitación magnética de un grado de libertad, que consiste en una viga actuada por un cojinete magnético activo en configuración pendular. Se obtiene su representación en espacio de estado y, tras una linealización tangente, su función de transferencia correspondiente. Se describen las características del dispositivo experimental, su instrumentación electrónica y mecanismo, particularmente el procedimiento de diseño del actuador electromagnético empleado, así como su caracterización para determinar la constante de proporcionalidad de la fuerza electromagnética. Finalmente, se muestra la simulación numérica e implementación experimental de esquemas clásicos de control para estabilizar en el punto de equilibrio del sistema.

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Section: Introductionunclassified

Modelado y control de un sistema de levitación magnética basado en un cojinete magnético activo

Cruz-Pegueros

Frías

Lozada‐Castillo

et al. 2017

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“…Calculation of the magnetic permanence of the air gap for each pole in a small sampling time weakens any control algorithm; the main reason for this situation is that calculation of the magnetic permanence of the air gap for each pole means that applied force by each pole is being obtained, when a time delay occurs in data acquisition because of the low processing speed of the data acquisition card (and it is commonly accepted that every magnetic levitation application requires very high processing data acquisition speed due to magnetic levitation's highly nonlinear nature [12]), proper actuation signals for coils cannot be produced. As an alternative solution, one may think that using a force sensor may be useful; however, many magnetic levitation systems are very vulnerable in terms of disturbance and, because of this, using a force sensor is not a good engineering implementation; in some cases, the electromagnetic force is generally measured by an additional system consisting of a copy of the levitating object located in a load cell [12,13].…”

Section: Introductionmentioning

confidence: 99%

“…As an alternative solution, one may think that using a force sensor may be useful; however, many magnetic levitation systems are very vulnerable in terms of disturbance and, because of this, using a force sensor is not a good engineering implementation; in some cases, the electromagnetic force is generally measured by an additional system consisting of a copy of the levitating object located in a load cell [12,13]. The only remaining reasonable technique for success of controlling magnetic levitation systems is estimating the magnetic force value for each pole.…”

Section: Introductionmentioning

confidence: 99%

Force and torque parameter estimation for a 4-pole hybrid electromagnet by ANFIS hybrid learning algorithm

Atlıhan¹,

Yalçin²,

Erkan³

2017

Turk J Elec Eng & Comp Sci

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Abstract:In this study, a force and torque estimation method based on an adaptive neuro-fuzzy inference system (ANFIS) has been developed to get rid of multiple integral calculations of air gap coefficients that cause time delay for magnetic levitation control applications. During magnetic levitation applications that contain a 4-pole hybrid electromagnet, multiple integral calculations have to be done for obtaining air gap permanence parameters, and these parameters are needed to calculate force and torque parameters that are produced by the poles of the hybrid electromagnet, which means, if time delay occurs for calculation of permanence parameters, actual force and actual torque values that are produced by hybrid electromagnet's poles cannot be exactly known; thus, the advantage of having an exact model of the system gets lost and, as a result, the controller's performance goes down. To address a solution, an ANFIS using a hybrid learning algorithm consisting of backpropagation and least-squares learning methods is proposed to estimate force and torque parameters using training data already obtained using multiple integral calculations before.

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“…In this scenario, Model Predictive Control (MPC) has gained increasing attention in the last few years and has been implemented in a variety of applications and in different forms to tackle issues of nonlinearities and variability of the operating conditions. A rich literature describes the potentialities of this approach in magnetic levitation [19][20][21][22][23]. In particular, MPC has shown an improved performance in compensating for gyroscopic effects [24] and revealed properties of good stability and robustness when compared with standard control architectures for the levitation of flywheels [25].…”

Section: Introductionmentioning

confidence: 99%

Offset-Free Model Predictive Control for Active Magnetic Bearing Systems

Bonfitto¹,

Molina²,

Tonoli³

et al. 2018

Actuators

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This paper presents the study of linear Offset-Free Model Predictive Control (OF-MPC) for an Active Magnetic Bearing (AMB) application. The method exploits the advantages of classical MPC in terms of stability and control performance and, at the same time, overcomes the effects of the plant-model mismatch on reference tracking. The proposed approach is based on a disturbance observer with an augmented plant model including an input disturbance estimation. Besides the abovementioned advantages, this architecture allows a real-time estimation of low-frequency disturbance, such as slow load variations. This property can be of great interest for a variety of AMB systems, particularly where the knowledge of the external load is important to regulate the behavior of the controlled plant. To this end, the paper describes the modeling and design of the OF-MPC architecture and its experimental validation for a one degree of freedom AMB system. The effectiveness of the method is demonstrated in terms of the reference tracking performance, cancellation of plant-model mismatch effects, and low-frequency disturbance estimation.

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Nonlinear model predictive control of a magnetic levitation system

Cited by 99 publications

References 35 publications

Modelado y control de un sistema de levitación magnética basado en un cojinete magnético activo

Modelado y control de un sistema de levitación magnética basado en un cojinete magnético activo

Force and torque parameter estimation for a 4-pole hybrid electromagnet by ANFIS hybrid learning algorithm

Offset-Free Model Predictive Control for Active Magnetic Bearing Systems

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