Active magnetic bearing: A new step for model-free control

Miras, Jérôme de; Join, Cédric; Fliess, Michel; Riachy, Samer; Bonnet, Stéphane

doi:10.1109/cdc.2013.6761072

Cited by 41 publications

(24 citation statements)

References 22 publications

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“…MF algorithm had been validated in different control applications, such as: shape memory alloys [4], dc/dc converters [5], active magnetic bearing [6] planar manipulator [7], and to control the quadrotor vehicle [8].…”

Section: Introductionmentioning

confidence: 99%

Model-free observer for state estimation applied to a quadrotor

Younes

Noura

Muflehi

et al. 2015

2015 International Conference on Unmanned Aircraft Systems (ICUAS)

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In this paper, the Nonlinear Model-Free Observer (NMFO) is proposed to estimate the states of the quadrotor vehicle. NMFO is an integrated structure of the Model-Free technique and the nonlinear Thau observer. The Thau observer will accommodate for the nonlinearities in the quadrotor model and MFO will compensate for the un-modeled dynamics, modeling errors, and the system uncertainties.Flight test results have validated the proposed observer performance towards fault-free and actuator fault conditions.

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Section: Introductionmentioning

confidence: 99%

Model-free observer for state estimation applied to a quadrotor

Younes

Noura

Muflehi

et al. 2015

2015 International Conference on Unmanned Aircraft Systems (ICUAS)

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“…• ν ≥ 1 is the derivation order, • α ∈ R is chosen such that αu and z (ν) are of the same order of magnitude, • ν and α are chosen by the practitioner. Until now from our knowledge, in the context of model-free control, the example of magnetic bearings [11] with their low friction, provides the only instance where the order ν = 2 is necessary.…”

Section: Or Control Purposesmentioning

confidence: 99%

An Efficient Model-Free Setting for Longitudinal and Lateral Vehicle Control: Validation Through the Interconnected Pro-SiVIC/RTMaps Prototyping Platform

Menhour

d’Andréa-Novel

Fliess

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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Abstract-In this paper, the problem of tracking desired longitudinal and lateral motions for a vehicle is addressed. Let us point out that a "good" modeling is often quite difficult or even impossible to obtain. It is due for example to parametric uncertainties, for the vehicle mass, inertia or for the interaction forces between the wheels and the road pavement. To overcome this type of difficulties, we consider a model-free control approach leading to "intelligent" controllers. The longitudinal and the lateral motions, on one hand, and the driving/braking torques and the steering wheel angle, on the other hand, are respectively the output and the input variables. An important part of this work is dedicated to present simulation results with actual data. Actual data, used in Matlab as reference trajectories, have been previously recorded with an instrumented Peugeot 406 experimental car. The simulation results show the efficiency of our approach. Some comparisons with a nonlinear flatnessbased control in one hand, and with a classical PID control in another hand confirm this analysis. Other virtual data have been generated through the interconnected platform SiVIC/RTMaps, which is a virtual simulation platform for prototyping and validation of advanced driving assistance systems.

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“…where • ν ≥ 1 is the derivation order, • α ∈ R is chosen such that αu and y (ν) are of the same order of magnitude, • ν and α are chosen by the practitioner. Until now from our knowledge, in the context of model-free control, the example of magnetic bearings [3] with their low friction, provides the only instance where the order ν = 2 is necessary. Some comments and assumptions on F can be done:…”

Section: A the Ultra-local Modelmentioning

confidence: 99%

“…SiVIC is a professional software of CIVITEC (http://www.civitec.com) 3. RT-Maps is developed by Intempora (http://www.intempora.com).…”

mentioning

confidence: 99%

A new model-free design for vehicle control and its validation through an advanced simulation platform

Menhour

d’Andréa-Novel

Fliess

et al. 2015

2015 European Control Conference (ECC)

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A new model-free setting and the corresponding "intelligent" P and PD controllers are employed for the longitudinal and lateral motions of a vehicle. This new approach has been developed and used in order to ensure simultaneously a best profile tracking for the longitudinal and lateral behaviors. The longitudinal speed and the derivative of the lateral deviation, on one hand, the driving/braking torque and the steering angle, on the other hand, are respectively the output and the input variables. Let us emphasize that a "good" mathematical modeling, which is quite difficult, if not impossible to obtain, is not needed for such a design. An important part of this publication is focused on the presentation of simulation results with actual and virtual data. The actual data, used in Matlab as reference trajectories, have been obtained from a properly instrumented car (Peugeot 406). Other virtual sets of data have been generated through the interconnected platform SiVIC/RTMaps. It is a dedicated virtual simulation platform for prototyping and validation of advanced driving assistance systems.

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Active magnetic bearing: A new step for model-free control

Cited by 41 publications

References 22 publications

Model-free observer for state estimation applied to a quadrotor

Model-free observer for state estimation applied to a quadrotor

An Efficient Model-Free Setting for Longitudinal and Lateral Vehicle Control: Validation Through the Interconnected Pro-SiVIC/RTMaps Prototyping Platform

A new model-free design for vehicle control and its validation through an advanced simulation platform

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