Introduction

Wang, Danwei; Ye, Yongqiang; Zhang, Bin

doi:10.1007/978-981-4585-60-6_1

Cited by 1 publication

(2 citation statements)

References 79 publications

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“…Where the G ( z ) is the transfer function of the plant, v ( z ) is the disturbances, y j ( z ) and U j ( z ) are the output and input, respectively. The equation of tracking error can be obtained as follow (Wang et al , 2014): …”

Section: Theory Of Adaptive Resonance Suppression Iterative Learning ...mentioning

confidence: 99%

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An iterative learning control with learnable band extension for the nanopositioning stage

Huang

Yang

2022

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Purpose Due to the advantages of fast response, high positioning precision and large stiffness, the piezoelectric-actuated nanopositioning stage is widely used in the micro/nanomachining fields. However, due to the inherent nonlinear hysteresis of the piezoelectric-actuator, the positioning accuracy of nanopositioning stage is greatly degraded. Besides, the nanopositioning stage is always performed with repetitive trajectories as the reference signals in applications, which makes the hysteresis behavior periodic. To this end, an adaptive resonance suppression iterative learning control (ARS-ILC) is proposed to address the hysteresis effect. With this effort, the positioning accuracy of the nanopositioning stage is improved. Design/methodology/approach The hysteresis behavior is identified by the Prandtl–Ishlinskii model. By establishing a convergence function, it is demonstrated that the learnable band of ILC is restricted by the lightly damping resonance of nanopositioning stage. Then, an adaptive notch filter (ANF) with constrained poles and zeros is adopted to suppress the resonant peak. Finally, online stability supervision (OSS) is used to ensure that the estimated frequency converges to the resonant frequency. Findings A series of experiments were carried out in the nanopositioning stage, and the results validated that the OSS is available to ensure the convergence of the ANF. Furthermore, the learnable band was extended via ARS-ILC; thus, the hysteresis behavior of nanopositioning stage has been canceled. Originality/value Due to high accuracy and easy implementation, the ARS-ILC can be used in not only nanopositioning stage control but other fabrication process control with repetitive motion.

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Section: Theory Of Adaptive Resonance Suppression Iterative Learning ...mentioning

confidence: 99%

“…The detailed theoretical derivation can be found in Wang et al (2014). This issue of error decaying is called error convergence.…”

Section: Theory Of Adaptive Resonance Suppression Iterative Learning ...mentioning

confidence: 99%

An iterative learning control with learnable band extension for the nanopositioning stage

Huang

Yang

2022

View full text Add to dashboard Cite

show abstract

Introduction

Cited by 1 publication

References 79 publications

An iterative learning control with learnable band extension for the nanopositioning stage

An iterative learning control with learnable band extension for the nanopositioning stage

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