A Survey on Modelling and Compensation for Hysteresis in High Speed Nanopositioning of AFMs: Observation and Future Recommendation

Armin, Maniza; Roy, Priyo Nath; Das, Sajal K.

doi:10.1007/s11633-020-1225-4

Cited by 18 publications

(13 citation statements)

References 126 publications

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“…However, it accurately represents the machine's basic characteristics and performance. It should be noted that the proposed method is also applicable to all types of PMSM [18]. Hence, this model will serve as the reference machine model.…”

Section: ░2 Development Of Mathematical Modelmentioning

confidence: 99%

Performance Analysis of Permanent Magnet Synchronous Machine due to Winding Failures

Kulkarni

Thosar

2021

IJEER

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The paper describes winding failures in Permanent Magnet Synchronous Motor (PMSM) which are preferred in Electric Vehicles. Drive can be designed with fault tolerance to continue to operate under occurrence of faults. Winding insulation failure is responsible for some of the most serious faults. A method for detecting open circuit of winding, short circuit of winding or turn to turn short circuit in winding may result as a failure of winding insulation is presented. Further a technique for continued post-fault operation of the drive is discussed based upon mathematical model. The detection method operates in real time without the use of additional sensors and is sensitive enough to detect the presence of an air-gap between turns.

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Section: ░2 Development Of Mathematical Modelmentioning

confidence: 99%

Performance Analysis of Permanent Magnet Synchronous Machine due to Winding Failures

Kulkarni

Thosar

2021

IJEER

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“…Hysteresis models for PEAs are mainly classified in two main categories: physical and phenomenological [19]. The first-mentioned group is a description of the ferromagnetic effect that produces the non-linearity, although the material dependency and complex numerical solutions are the downsides of these theories [20,21]. In regards to the phenomenological, the sub-classification is related to the ones based on differential equations (Dunhem [22], Backslash [23] and Bouc-Wen [24]), operator models (Preisach [5], Prandtl-Ishlinskii [25] and Krasnoselskii-Pokrovskii [26]) and polynomial models [27].…”

Section: Introductionmentioning

confidence: 99%

Advanced Trajectory Control for Piezoelectric Actuators Based on Robust Control Combined with Artificial Neural Networks

et al. 2021

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In applications where high precision in micro- and nanopositioning is required, piezoelectric actuators (PEA) are an optimal micromechatronic choice. However, the accuracy of these devices is affected by a natural phenomenon called “hysteresis” that even increases the instability of the system. This anomaly can be counteracted through a material re-shape or by the design of a control strategy. Through this research, a novel control design has been developed; the structure contemplates an artificial neural network (ANN) feedforward to contract the non-linearities and a robust close-loop compensator to reduce the unmodelled dynamics, uncertainties and perturbations. The proposed scheme was embedded in a dSpace control platform with a Thorlabs PEA; the parameters were tuned online through specific metrics. The outcomes were compared with a conventional proportional-integral-derivative (PID) controller in terms of control signal and tracking performance. The experimental gathered results showed that the advanced proposed strategy had a superior accuracy and chattering reduction.

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“…There are two different approaches for hysteresis modeling: mathematical-and physicsbased [58]. The first kind contemplates the most used ones, like Preisach, Prandtl-Ishlinksii, and Krasnosel'skii-Pokrovkii models [59][60][61], which are based on hysteron operators.…”

Section: Introductionmentioning

confidence: 99%

“…Despite its complexity, recent cutting edge uni-axial phenomenological models have been developed which are rate and material independent and they only require a history variable [64,65]. The physics-based ones are known as the Domain Wall and the Jiles-Atherton model; these are acknowledged for their description of hysteresis present in magnetic materials and might be unsuitable since they are material-dependent models [58].…”

Section: Introductionmentioning

confidence: 99%

High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

et al. 2021

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Piezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.

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A Survey on Modelling and Compensation for Hysteresis in High Speed Nanopositioning of AFMs: Observation and Future Recommendation

Cited by 18 publications

References 126 publications

Performance Analysis of Permanent Magnet Synchronous Machine due to Winding Failures

Performance Analysis of Permanent Magnet Synchronous Machine due to Winding Failures

Advanced Trajectory Control for Piezoelectric Actuators Based on Robust Control Combined with Artificial Neural Networks

High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

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