Feedforward Control of Piezoelectric Ceramic Actuators Based on PEA-RNN

Xiong, Yongcheng; Jia, Wei; Zhang, Limin; Zhao, Ying; Zheng, Lei

doi:10.3390/s22145387

Cited by 8 publications

(8 citation statements)

References 16 publications

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“…PEA actuator (T434-A4-201, Piezo Systems, Inc., Cambridge, MA, USA) 0.62 RNN [25] The feedforward compensator was developed by the deep learning method (RNN).…”

Section: Comparison With Other Relevant Workmentioning

confidence: 99%

“…The results are shown in Table 6 , where we can see that our method improves tracking performance on nanopositioning systems and outperforms in comparison with the other methods in terms of average RMSE. The compensation method that is based only on the improved inverse Preisach [ 52 ] method has low accuracy (RMSE of =0.15 μm), but it is better than the compensation method based on the recurrent neural networks (RNNs) [ 25 ], which produced an average RMSE of 0.465 μm. The FF-FB control method based on the LSSVM algorithm without modeling hysteresis [ 28 ] produced the highest RMSE value (0.62 μm).…”

Section: Comparison With Other Relevant Workmentioning

confidence: 99%

“…However, the use of the inverse of these models for designing feedforward hysteresis compensators does have some limitations. For instance, some models fall into local optimums and lead to reduced search accuracy [ 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Precision Motion Control of a Piezoelectric Actuator via a Modified Preisach Hysteresis Model and Two-Degree-of-Freedom H-Infinity Robust Control

2023

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The nonlinear hysteresis phenomenon can occur in piezoelectric-driven nanopositioning systems and can lead to reduced positioning accuracy or result in a serious deterioration of motion control. The Preisach method is widely used for hysteresis modeling; however, for the modeling of rate-dependent hysteresis, where the output displacement of the piezoelectric actuator depends on the amplitude and frequency of the input reference signal, the desired accuracy cannot be achieved with the classical Preisach method. In this paper, the Preisach model is improved using least-squares support vector machines (LSSVMs) to deal with the rate-dependent properties. The control part is then designed and consists of an inverse Preisach model to compensate for the hysteresis nonlinearity and a two-degree-of-freedom (2-DOF) H-infinity feedback controller to enhance the overall tracking performance with robustness. The main idea of the proposed 2-DOF H-infinity feedback controller is to find two optimal controllers that properly shape the closed-loop sensitivity functions by imposing some templates in terms of weighting functions in order to achieve the desired tracking performance with robustness. The achieved results with the suggested control strategy show that both hysteresis modeling accuracy and tracking performance are significantly improved with average root-mean-square error (RMSE) values of 0.0107 μm and 0.0212 μm, respectively. In addition, the suggested methodology can achieve better performance than comparative methods in terms of generalization and precision.

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“…PEA actuator (T434-A4-201, Piezo Systems, Inc., Cambridge, MA, USA) 0.62 RNN [25] The feedforward compensator was developed by the deep learning method (RNN).…”

Section: Comparison With Other Relevant Workmentioning

confidence: 99%

Section: Comparison With Other Relevant Workmentioning

confidence: 99%

See 1 more Smart Citation

Precision Motion Control of a Piezoelectric Actuator via a Modified Preisach Hysteresis Model and Two-Degree-of-Freedom H-Infinity Robust Control

2023

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“…The only factor affecting the model output in the rate-independent method is the amplitude of the input voltage. The major benefit of these models is that they are easy to identify and simple to implement [ 22 , 23 ]. 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.…”

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 hysteresis model-based approach is primarily used [20,21]. It has been shown that Prandtl-Ishlinskii(PI) hysteresis modeling is one method often used to describe hysteresis [22], and it typically describes a continuous loop of hysteresis that is rateindependent [23]. This PI hysteresis modeling has the advantage of being simple, and its hysteresis compensator can be calculated analytically [24].…”

Section: Introductionmentioning

confidence: 99%

An Identification Method for Dynamic Systems with SCNs Hysteresis Compensator: Application to the Inchworm Piezo Motor

Han,

Xu,

et al. 2023

Preprint

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It is widely known that inchworm piezo motors are being used in ultra-precision detection platforms where high clamping force and nano-precision are required. This paper examines and proposes a dynamic model of the inchworm piezo motor to improve the performance and controller. Traditional identification strategies have not been adopted for calculating parameters for the dynamic model exhibiting hysteresis nonlinearity. In addition, hysteresis deteriorates the accuracy of positioning of inchworm piezo motors. It has been proposed to solve these problems using a feedforward controller, a hysteresis compensator based on a stochastic configuration networks algorithm (SCNs). Using the SCNs algorithm, both voltage and displacement inputs can be used to describe the nonlinear memory of hysteresis, and the kernel function provides a flexible and accurate representation of this behavior. This method enables the dynamic model parameters of the inchworm piezo motor to be identified using frequency sweeps. A proportional-integral (PI) controller is used to verify the positioning accuracy, and experimental results show that an inchworm piezo motor has a positioning accuracy of about 1nm when the laser interference is low with a 10M Hz adoption rate and 0.1nm resolution.

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Feedforward Control of Piezoelectric Ceramic Actuators Based on PEA-RNN

Cited by 8 publications

References 16 publications

Precision Motion Control of a Piezoelectric Actuator via a Modified Preisach Hysteresis Model and Two-Degree-of-Freedom H-Infinity Robust Control

Precision Motion Control of a Piezoelectric Actuator via a Modified Preisach Hysteresis Model and Two-Degree-of-Freedom H-Infinity Robust Control

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

An Identification Method for Dynamic Systems with SCNs Hysteresis Compensator: Application to the Inchworm Piezo Motor

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