Tracking Control of a Monolithic Piezoelectric Nanopositioning Stage using an Integrated Sensor.

Omidbeike, Meysam; Teo, Yik R.; Yong, Yuen Kuan; Fleming, Andrew J.

doi:10.1016/j.ifacol.2017.08.2455

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…Except for the disturbances problem of online estimation introduced in Section 1, the same output of a stable closed-loop system under the input signals with different feedback effects also hinders the use of closed-loop online system identification. These issues lead to increased union error or even unstable online estimation [25]. According to the system identification theory, in order to ensure the successful implementation of closed-loop online system identification, at least one of the conditions from a1 to a3 must be satisfied.…”

Section: Description Of the Online Frequency Estimate Problemmentioning

confidence: 99%

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Adaptive Notch Filter for Piezo-Actuated Nanopositioning System via Position and Online Estimate Dual-Mode

Huang

2021

Micromachines

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Due to the excellent advantages of high speed, high precision, and driving force, piezoelectric actuators nanopositioning systems have been widely used in various micro/nanomachining fields. However, the inherent resonance dynamic of the nanopositioning system generated by the flexure-hinge greatly deteriorates the positioning performance and limits the closed-loop bandwidth. Even worse, the notch filter for eliminating the effect of resonance does not work due to the varying resonant frequency resulting from the external disturbance or mass load. To this end, an adaptive notch filter for piezo-actuated nanopositioning system via position and online estimate dual-mode (POEDM) has been proposed in this paper, which can estimate the varying resonant frequency in real-time and suppress the resonance to improve the closed-loop bandwidth. First, a novel variable forgetting factor recursive least squares (VFF-RLS) algorithm for estimating resonant frequency online is presented, which is robust to the noise and provides the performances of both fast tracking and stability. Then, a POEDM method is proposed to achieve the online identification of the resonant frequency in the presence of noise and disturbance. Finally, a series of validation simulations are carried out, and the results indicate that, the frequency of input signal and the bandwidth have been achieved up to 12.5% and 87.5% of the first resonant frequency, respectively.

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Section: Description Of the Online Frequency Estimate Problemmentioning

confidence: 99%

“…where J(k) is formed as weighted summation. In order to demonstrate the forgetting property of ( 6), a memory time constant T 0 is defined in [25]:…”

Section: Design Of Variable Forgetting Factor Rls Algorithmmentioning

confidence: 99%

Adaptive Notch Filter for Piezo-Actuated Nanopositioning System via Position and Online Estimate Dual-Mode

Huang

2021

Micromachines

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“…Smart material based actuators are new types of actuators different from the traditional electromagnetic actuators. Among these smart actuators, the piezoelectric actuator is widely used in the field of micro-/nano-positioning and manipulation [1][2][3][4][5][6], biomedical robotics and extreme environments [7][8][9][10] and optics [11][12][13] due to its extraordinary characteristics such as nanometer-scale displacement resolution, nonexistent friction and fast response.…”

Section: Introductionmentioning

confidence: 99%

Hysteresis Modelling and Feedforward Control of Piezoelectric Actuator Based on Simplified Interval Type-2 Fuzzy System

Zhang

et al. 2020

Sensors

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The piezoelectric actuator is indispensable for driving the micro-manipulator. In this paper, a simplified interval type-2 (IT2) fuzzy system is proposed for hysteresis modelling and feedforward control of a piezoelectric actuator. The partial derivative of the output of IT2 fuzzy system with respect to the modelling parameters can be analytically computed with the antecedent part of IT2 fuzzy rule specifically designed. In the experiments, gradient based optimization was used to identify the IT2 fuzzy hysteresis model. Results showed that the maximum error of model identification is 0.42% with only 3 developed IT2 fuzzy rules. Moreover, the model validation was conducted to demonstrate the generalization performance of the identified model. Based on the analytic inverse of the developed model, feedforward control experiment for tracking sinusoidal trajectory of 20 Hz was carried out. As a result, the hysteresis effect of the piezoelectric actuator was reduced with the maximum tracking error being 4.6%. Experimental results indicated an improved performance of the proposed IT2 fuzzy system for hysteresis modelling and feedforward control of the piezoelectric actuator.

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Sensing and Decentralized Control of a Five-Axis Monolithic Nanopositioning Stage

2020

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Tracking Control of a Monolithic Piezoelectric Nanopositioning Stage using an Integrated Sensor.

Cited by 8 publications

References 10 publications

Adaptive Notch Filter for Piezo-Actuated Nanopositioning System via Position and Online Estimate Dual-Mode

Adaptive Notch Filter for Piezo-Actuated Nanopositioning System via Position and Online Estimate Dual-Mode

Hysteresis Modelling and Feedforward Control of Piezoelectric Actuator Based on Simplified Interval Type-2 Fuzzy System

Sensing and Decentralized Control of a Five-Axis Monolithic Nanopositioning Stage

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