A rate-dependent KP modeling and direct compensation control technique for hysteresis in piezo-nanopositioning stages

Xu, Rui; Tian, Dapeng; Zhou, Miaolei

doi:10.1177/1045389x211023583

Cited by 12 publications

(4 citation statements)

References 31 publications

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“…We define e 1 (s) = y(s) − y m (s) as the error between output values of P-AS and the reference model. By combining (7), (10), and ( 14), we obtain:…”

Section: Hac Design Based On Hysteresis Compensatormentioning

confidence: 99%

“…The response of RD hysteresis characteristic exhibits a special multi-value mapping relationship, where the P-AS output depends on the current input and the previous output of P-AS [9]. In addition, the RD characteristic indicates that the frequency of the P-AS input is one of the important factors affecting their output [10]. Therefore, current research focuses on developing reasonable control strategies to depress the hysteresis characteristic of the P-AS [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Adaptive Controller Design with Hysteresis Compensator for a Piezo-Actuated Stage

et al. 2022

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Piezo-actuated stage (P-AS) has become the topic of considerable interest in the realm of micro/nanopositioning technology in the recent years owing to its advantages, such as high positioning accuracy, high response speed, and large output force. However, rate-dependent (RD) hysteresis, which is an inherent nonlinear property of piezoelectric materials, considerably restricts the application of P-AS. In this research paper, we develop a Hammerstein model to depict the RD hysteresis of P-AS. An improved differential evolution algorithm and a least-squares algorithm are used to identify the static hysteresis sub-model and the dynamic linear sub-model for the Hammerstein model, respectively. Then, a hysteresis compensator based on the inverse Bouc–Wen model is designed to compensate for the static hysteresis of the P-AS. However, the inevitable modeling error presents a hurdle to the hysteresis compensation. In addition, external factors, such as environmental noise and measurement errors, affect the control accuracy. To overcome these complications, a hybrid adaptive control approach based on the hysteresis compensator is adopted to increase the control accuracy. The closed-loop system stability is analyzed with the help of the Lyapunov stability theory. Finally, experimental results indicate that the raised control approach is effective for the accurate positioning of P-AS.

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“…We define e 1 (s) = y(s) − y m (s) as the error between output values of P-AS and the reference model. By combining (7), (10), and ( 14), we obtain:…”

Section: Hac Design Based On Hysteresis Compensatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Adaptive Controller Design with Hysteresis Compensator for a Piezo-Actuated Stage

et al. 2022

Self Cite

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“…For example, [16] proposed a three-dimensional micromechanical model for describing the rate-dependent properties of piezoelectric materials. The rate-dependent Prandtl-Ishlinskii model [17] and Krasnosel'skii-Pokrovkii model [18] are also proposed to describe the rate-dependent hysteresis nonlinearity of various piezoelectric material actuators and have achieved a great rate-dependent hysteresis modeling effect of smart material systems. However, since the input to the hysteresis part of the Bouc-Wen model contains not only the positional information of the input values, but also the velocity information of the input values, it has certain rate-dependent properties.…”

Section: Introductionmentioning

confidence: 99%

High-Precision Position Tracking Control with a Hysteresis Observer Based on the Bouc–Wen Model for Smart Material-Actuated Systems

Zhao,

Li,

Cao

et al. 2024

Actuators

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The Bouc–Wen model has been widely adopted to describe hysteresis nonlinearity in many smart material-actuated systems, such as piezoelectric actuators, shape memory alloy actuators, and magnetorheological dampers. For effective control design, it is of interest to estimate the hysteresis state that is not measurable. In this paper, the design of a state observer for the Bouc–Wen model is presented. It is shown that, with sufficiently high observer gains, the state estimate error, including that for the hysteresis state, converges to zero exponentially fast. The utility of the proposed hysteresis observer is illustrated in the design of a high precision output-feedback position tracking controller, and the resulting tracking error is shown to decay exponentially via Lyapunov analysis. Simulation and experimental results show that the proposed hysteresis observer and the high precision position tracking controller outperform a traditional extended state observer and the corresponding tracking controller, respectively.

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“…Within this method, three different model theories can be distinguished: (1) the theory of dynamic modeling, which is represented mathematically by a collection of differential equations. Some model examples include the Jiles–Atherton model [ 24 ], the Duhem model [ 25 ], the Bouc–Wen model [ 26 ], the Backlash-Like model [ 27 ], the Maxwell model [ 28 ], and the approximated Polynomial model [ 29 ]; (2) the operator modeling theory, including Prandtl–Ishlinskii (PI) [ 30 ], Krasnoselskii–Pokrovskii (KP) [ 31 ], and Preisach [ 32 ] models; (3) the intelligent modeling theory. This approach models systems with hysteresis based on the concept of the computational intelligence [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control

Uralde

Artetxe

Barambones

et al. 2023

Sensors

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Piezoelectric actuators (PEA) are high-precision devices used in applications requiring micrometric displacements. However, PEAs present non-linearity phenomena that introduce drawbacks at high precision applications. One of these phenomena is hysteresis, which considerably reduces their performance. The introduction of appropriate control strategies may improve the accuracy of the PEAs. This paper presents a high precision control scheme to be used at PEAs based on the model-based predictive control (MPC) scheme. In this work, the model used to feed the MPC controller has been achieved by means of artificial neural networks (ANN). This approach simplifies the obtaining of the model, since the achievement of a precise mathematical model that reproduces the dynamics of the PEA is a complex task. The presented approach has been embedded over the dSPACE control platform and has been tested over a commercial PEA, supplied by Thorlabs, conducting experiments to demonstrate improvements of the MPC. In addition, the results of the MPC controller have been compared with a proportional-integral-derivative (PID) controller. The experimental results show that the MPC control strategy achieves higher accuracy at high precision PEA applications such as tracking periodic reference signals and sudden reference change.

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A rate-dependent KP modeling and direct compensation control technique for hysteresis in piezo-nanopositioning stages

Cited by 12 publications

References 31 publications

Hybrid Adaptive Controller Design with Hysteresis Compensator for a Piezo-Actuated Stage

Hybrid Adaptive Controller Design with Hysteresis Compensator for a Piezo-Actuated Stage

High-Precision Position Tracking Control with a Hysteresis Observer Based on the Bouc–Wen Model for Smart Material-Actuated Systems

Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control

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