Intelligent integral backstepping sliding-mode control for piezo-flexural nanopositioning stage

Lin, Faa-Jeng; Lee, Shih-Yang

doi:10.1109/ifeec.2015.7361416

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…Cao and Chen [31] presented a hybrid control by integrating the inversion feedforward control and PIDbased sliding model control to compensate the hysteresis nonlinearity more effectively. Lin and Lee [32] developed an intelligent integral backstepping sliding-mode controller using a recurrent neural network to eliminate the hysteresis nonlinearity of piezo-driven nano-positioning stage. Ounissi et al [7] used the PID controller to eliminate the hysteresis nonlinearity in piezoelectric actuator and the maximum relative error was 5.3%.…”

Section: B Operator-based Modelsmentioning

confidence: 99%

Iterative Learning and Fractional Order PID Hybrid Control for a Piezoelectric Micro-Positioning Platform

Zhou

Zhang

et al. 2020

IEEE Access

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The piezoelectric micro-positioning platform(PMP) has advantages in high displacement resolution and fast response, however, the serious hysteresis nonlinearity in the PMP restricts its positioning accuracy. This paper presents a discretization of Krasnosel'skii-Pokrovskii model to describe the hysteresis nonlinearity of the PMP. Then, the density function is identified online by the adaptive linear neural network. To compensate the intrinsic hysteresis nonlinearity in the PMP, a feedforward compensation control method is implemented by an innovative iterative learning control algorithm. Moreover, a hybrid control method based on iterative learning and fractional order PID control is proposed to further improve the control accuracy. A series of comparative experiments are carried out to validate the feasibility and effectiveness of the proposed control method.INDEX TERMS Piezoelectric micro-positioning platform, hysteresis nonlinearity, iterative learning control, fractional order PID.

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Section: B Operator-based Modelsmentioning

confidence: 99%

Iterative Learning and Fractional Order PID Hybrid Control for a Piezoelectric Micro-Positioning Platform

Zhou

Zhang

et al. 2020

IEEE Access

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“…From the operation principle, AFM can scan samples including conductors and insulators, which later has become a popular measurement instrument in a lot of applications of biological science and nano-technology. It is known that AFM utilizes the atomic force between the sharp tip of the probe [3] and the measured sample surface to precisely reconstruct the three-dimensional profile of the measured sample with a xyznanopositioning stage [4].…”

Section: Introductionmentioning

confidence: 99%

Effective Tilting Angles for a Dual Probes AFM System to Achieve High-Precision Scanning

Lin

et al. 2016

IEEE/ASME Trans. Mechatron.

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Because of the everlasting promotion of micro/nano-fabrication techniques, the measurement of the feature contour of micro/nano-fabricated structures become an important issue. Atomic force microscopy (AFM) is a high accuracy measurement instrument that has been frequently used in measuring of micro/nano-fabricated structures. However, most conventional AFM systems use a single probe with a monotonic tilting angle to scan all kinds of sample profiles. This type of AFM design easily suffers from the so-called "side wall effect", and the scanning result will induce a distortion phenomenon at the corner part. To solve this problem, a novel dual probe AFM system is proposed in this paper. A highly flexible system structure is adopted in this work to create different tilting angle of each probe. With the method developed for obtaining the appropriate tilting angle, we set up the so-called "effective tilting angles" under different scanning scenarios. In addition, a useful merging method has been developed to stitch together the scanning results from two different probes out of two different scanning units. For scanning a standard grating, the error of sidewall angle from the scan image decreases form 27.3 % to 4.5 % when our method is compared with a traditional scan. Finally, by integrating the proposed scan method with a new raster based local scan strategy, we can achieve a high-throughput precision scan. In the scan of human blood cells, we not only can remove unnecessary scan area up to 61.04 % but also can improve sidewall distortion. A comprehensive series of experiments have been conducted to validate the scanning capability of the proposed methods on our self-developed AFM system.

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“…Ge et al [29] proposed a hybrid controller comprised of inverse Preisach model as feedforward compensation and PID control to improve the tracking accuracy for a piezoelectric actuated system, when the amplitude of reference displacement is 12 µm, the maximum error rate is 2.08%. While Lin et al [30] developed an intelligent integral backstepping sliding-mode controller using a recurrent neural network to eliminate the hysteresis nonlinearity of the piezo-driven nanopositioning stage. Xu [31] utilized the dynamical model of the piezo-stage to design a digital integral terminal sliding mode controller, the effectiveness of which was supported with experimental results.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Tracking Control for the Piezoelectric Actuated Stage Using the Krasnosel’skii-Pokrovskii Operator

Tian

Wang

2020

Micromachines

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In this paper, a discrete second order linear equation with the Krasnosel’skii-Pokrovskii (KP) operator is used to describe the piezoelectric actuated stage. The weights of the KP operators are identified by the gradient descent algorithm. To suppress the hysteresis nonlinearity of the piezoelectric actuated stage, this paper proposes an adaptive tracking control with the hysteresis decomposition on the designed error surface. The proposed adaptive tracking controller dispenses with any form of the feed-forward hysteresis compensation and the unknown parameters of the discrete second order linear equation are adaptively adjusted. Some simulations are implemented to verify the effectiveness of the KP operators, then a series of modeling and control experiments are carried out on the piezoelectric actuated stages experimental systems. The comparative experimental results verify the feasibility of the KP operators modeling method and the adaptive tracking control method.

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Intelligent integral backstepping sliding-mode control for piezo-flexural nanopositioning stage

Cited by 4 publications

References 9 publications

Iterative Learning and Fractional Order PID Hybrid Control for a Piezoelectric Micro-Positioning Platform

Iterative Learning and Fractional Order PID Hybrid Control for a Piezoelectric Micro-Positioning Platform

Effective Tilting Angles for a Dual Probes AFM System to Achieve High-Precision Scanning

Adaptive Tracking Control for the Piezoelectric Actuated Stage Using the Krasnosel’skii-Pokrovskii Operator

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