Automatic object release in magnetic and electrostatic levitation systems

West, Ewoud van; Yamamoto, Akio; Higuchi, Toshiro

doi:10.1016/j.precisioneng.2008.07.002

Cited by 18 publications

(7 citation statements)

References 10 publications

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“…The presence of such an integral loop allows to release the levitated objects automatically without any manual switching. This phenomena, which is described extensively in (van West et al, 2008), uses the integral controller wind up to reduce the holding force when a position error is temporarily forced to the levitated object. When, for example, the levitated object is brought to the desired location in the placing task, making contact at that location will reduce the air gap between levitator and object (the position error).…”

Section: Advances In Haptics 658mentioning

confidence: 99%

“…The integrator reduces the attractive force as long as there is a positive levitation error. If this error persists for some time, the controller output is influenced in such a way, that even if the error is relieved, re-levitation is no longer possible (van West et al, 2008). With this strategy, placing becomes more easy as it is realized automatically.…”

Section: Placingmentioning

confidence: 99%

See 1 more Smart Citation

Using Haptic Technology to Improve Non-Contact Handling: the “Haptic Tweezer” Concept

West¹,

Yamamoto²,

Higuchi³

2010

Advances in Haptics

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Section: Advances In Haptics 658mentioning

confidence: 99%

Section: Placingmentioning

confidence: 99%

Using Haptic Technology to Improve Non-Contact Handling: the “Haptic Tweezer” Concept

West¹,

Yamamoto²,

Higuchi³

2010

Advances in Haptics

Self Cite

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“…MLS has a practical significance in many engineering areas such as high-speed maglev trains, levitation of wind tunnel models, frictionless bearings, vibration isolation of sensitive machinery, suspension, and manipulation [1][2][3][4][5][6]. The widespread use of MLS is because it eliminates mechanical contact between moving and stationary parts.…”

Section: Introductionmentioning

confidence: 99%

Equilibrium Optimizer-Based Robust Sliding Mode Control of Magnetic Levitation System

Ismael¹,

Qasim²,

Noaman³

2021

JESA

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Magnetic Levitation System (MLS) objective is to levitate objects to the desired height without any contact. MLS is highly nonlinear and inherently unstable. Such a system imposes a challenge when designing robust and high-performance controllers. This paper presents the design of a Sliding Mode (SM) controller with an Integral term called SM-I controller to achieve the desired levitation against nonlinearities and uncertainties of the system. The controller parameters are tuned using the Equilibrium Optimizer (EO) algorithm. The Effectiveness of the proposed controller is validated by simulation results. Simulations are performed for servo tracking with and without perturbations in the MLS parameters. The proposed controller is compared with the conventional SM, LQR, and PID controllers to show its superiority. The results prove that the SM-I is more efficient than the other controllers.

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“…The most popular usage areas are magnetic bearings [1][2][3][4], transportation systems [5][6][7], and actuation of objects in micro-and nanoscales [8,9]. Because there is no mechanical contact between levitation system and levitating object, consisting of a ferromagnetic material, mechanical friction has almost no effect on system parameters (except air friction, which has a comparatively low value) on the levitating object [10]. However, there are also some disadvantages, such as the system model is highly nonlinear, and even though some methods may be useful for linearization at some certain points, obtained linear models show unstable system behavior [10,11].…”

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.

show abstract

Automatic object release in magnetic and electrostatic levitation systems

Cited by 18 publications

References 10 publications

Using Haptic Technology to Improve Non-Contact Handling: the “Haptic Tweezer” Concept

Using Haptic Technology to Improve Non-Contact Handling: the “Haptic Tweezer” Concept

Equilibrium Optimizer-Based Robust Sliding Mode Control of Magnetic Levitation System

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

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