Automatic Hysteresis Modeling of Piezoelectric Micromanipulator in Vision-Guided Micromanipulation Systems

Zhang, Yan Liang; Han, Ming Li; Meng, Yu Song; Shee, Cheng Yap; Ang, Wei Tech

doi:10.1109/tmech.2011.2106136

Cited by 48 publications

(21 citation statements)

References 23 publications

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“…Based on this model, a viscoplastic model for the clutch prototype discussed previously was obtained in [2]. According to this model, the shear stress of the fluid can be controlled with the applied field as τ = τ y (H) + η dv dz , τ > τ y (18) where τ is the shear stress, τ y is the field-dependent yield stress, H is the magnetic field intensity, η is the Newtonian viscosity, and dv dz is the velocity gradient in the direction of the field. The velocity gradient in (18) can be assumed constant under conditions discussed in [2], yielding τ = τ y (H) + ηγ(r), τ > τ y (19) Fig.…”

Section: B Magnetic Field Torque: Bingham Modelmentioning

confidence: 99%

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Adaptive Modeling of a Magnetorheological Clutch

Yadmellat

Kermani

2014

IEEE/ASME Trans. Mechatron.

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In this paper, a new open-loop model for a magnetorheological-based actuator is presented. The model consists of two parts relating the output torque of the actuator to its internal magnetic field, and the internal magnetic field to the applied current. Each part possesses its own hysteretic behavior. The first part uses a novel nonlinear adaptive model that relates the internal magnetic field to the applied current. The second part uses an open-loop Bingham model to relate the output torque to an internal magnetic field. The model facilitates accurate control of the actuator using its input current. It also eliminates the need for force/torque sensors for providing feedback signals. The accuracy of the constructed model is validated through simulations. The model is assessed against a widely accepted hysteresis modeling approach, known as the Preisach model and its advantages are highlighted. Experimental results using the prototyped actuation mechanism further verify the accuracy of the model and demonstrate its effectiveness.Index Terms-Adaptive modeling, hysteresis, magnetorheological fluids (MRFs), smart actuators.

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Section: B Magnetic Field Torque: Bingham Modelmentioning

confidence: 99%

“…However, these models are computation-and/or storage-intensive. Among other hysteresis models are the Prandtl-Ishlinskii [16]- [18] and Krasnoselskii-Pokrovskii [19] models. The Prandtl-Ishlinskii model can exhibit neither asymmetric hysteresis loop nor saturated hysteresis output [20].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Modeling of a Magnetorheological Clutch

Yadmellat

Kermani

2014

IEEE/ASME Trans. Mechatron.

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“…Nanopositioning stages are used for a wide and growing scope of applications at the microscale, such as micromanipulation, MEMS (Micro-Electro-Mechanical-Systems), mini-invasive surgery, biological and biomedical characterization [1][2][3][4]. Most of these applications require one to control very accurately the relative position and/or trajectories between a sample (for example, component to handle, tissue or living cell) and a tool used for manipulation or characterization purposes.…”

Section: Introductionmentioning

confidence: 99%

Calibration of Nanopositioning Stages

2015

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Accuracy is one of the most important criteria for the performance evaluation of microand nanorobots or systems. Nanopositioning stages are used to achieve the high positioning resolution and accuracy for a wide and growing scope of applications. However, their positioning accuracy and repeatability are not well known and difficult to guarantee, which induces many drawbacks for many applications. For example, in the mechanical characterisation of biological samples, it is difficult to perform several cycles in a repeatable way so as not to induce negative influences on the study. It also prevents one from controlling accurately a tool with respect to a sample without adding additional sensors for closed loop control. This paper aims at quantifying the positioning repeatability and accuracy based on the ISO 9283:1998 standard, and analyzing factors influencing positioning accuracy onto a case study of 1-DoF (Degree-of-Freedom) nanopositioning stage. The influence of thermal drift is notably quantified. Performances improvement of the nanopositioning stage are then investigated through robot calibration (i.e., open-loop approach). Two models (static and adaptive models) are proposed to compensate for both geometric errors and thermal drift. Validation experiments are conducted over a long period (several days) showing that the accuracy of the stage is improved from typical micrometer range to 400 nm using the static model and even down to 100 nm using the adaptive model. In addition, we extend the 1-DoF calibration to multi-DoF with a case study of a 2-DoF nanopositioning robot. Results demonstrate that the model efficiently improved the 2D accuracy from 1400 nm to 200 nm.

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“…So far, increasing the accuracy and the repeatability of micromanipulation position controllers still remains as a challenge. Three main kinds of controllers are reported on this topic: 1) The feedback controllers [1], which use feedback sensors, are model-free but have the drawback of phase lag to apply in applications requiring high-frequency response; 2) The feed-forward controllers [2], which generally construct a feed-forward compensation scheme after building a model of the system, do no have phase lag but need a very accurate model; 3) The feed-forward approach has also been used with feedback approaches to harness the advantages of both [3].…”

Section: Introductionmentioning

confidence: 99%

Increasing the accuracy and the repeatability of position control for micromanipulations using Heteroscedastic Gaussian Processes

Dong

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Many recent studies describe micromanipulation systems by using complex Analytic Forward Models (AFM), but such models are difficult to build and incapable of describing unmodelable factors, such as manufacturing defects. In this work, we propose the Enhanced Analytic Forward Model (EAFM), an integrated model of the AFM and the Heteroscedastic Gaussian Processes (HGP). The EAFM can compensate the shortfalls of the AFM by training the HGP on the residual of the AFM. This also allows the HGP to learn the repeatability of the micromanipulation system. Based on the EAFM, we further contribute an optimal position controller for improving the accuracy and the repeatability. This optimal EAFM controller is implemented and tested on a three degreeof-freedom micromanipulator based micromanipulation system. Two sets of real-world experiments are carried out to verify our method. The results demonstrate that the controller using EAFM can statistically achieve higher accuracy and repeatability than solely using the AFM.

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Automatic Hysteresis Modeling of Piezoelectric Micromanipulator in Vision-Guided Micromanipulation Systems

Cited by 48 publications

References 23 publications

Adaptive Modeling of a Magnetorheological Clutch

Adaptive Modeling of a Magnetorheological Clutch

Calibration of Nanopositioning Stages

Increasing the accuracy and the repeatability of position control for micromanipulations using Heteroscedastic Gaussian Processes

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