Neural network adaptive control of nonlinear systems preceded by hysteresis

Zhao, Xuelong; Su, Qiang; Chen, Shengxin; Tan, Yonghong

doi:10.1177/1045389x20948605

Cited by 4 publications

(3 citation statements)

References 44 publications

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“…Elman neural network–based hysteresis model is presented in Zhao et al (2020) and a dynamic hysteretic operator is used to handle the multivalued problem of hysteresis. To avoid computing inverse hysteresis model a neural network based adaptive controller scheme is presented in Zhao et al (2021). Although the conventional neural network based modeling and control methods are proven to be effective but training time is too long which increases computation burden.…”

Section: Introductionmentioning

confidence: 99%

Neural network based predictive control with optimized search space for dynamic tracking of a piezo-actuated nano stage

Ahmed,

Yan,

Zhang

2023

Journal of Intelligent Material Systems and Structures

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This paper presents an intelligent modified predictive control approach with squeezed search space, for tracking control of piezo-actuated nano stage. The model obtained from the gray box neural network is first dynamically linearized to avoid calculation of inverse hysteresis model. The optimum control values of the previous control cycle are used to construct a squeezed search space, which reduces the computation burden and improves the tracking control performance. The effectiveness of the proposed scheme is verified theoretically by deriving a convergence analysis and by experimental results. The results show that the proposed approach significantly improves the dynamic tracking performance for high-frequency reference signals than existing results in the literature.

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Section: Introductionmentioning

confidence: 99%

Neural network based predictive control with optimized search space for dynamic tracking of a piezo-actuated nano stage

Ahmed,

Yan,

Zhang

2023

Journal of Intelligent Material Systems and Structures

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“…The phenomenology-based model can also be roughly divided into three main groups [ 17 ]: differential-based models, such as the Bouc-wen [ 18 ] model, the Duhem [ 19 ] model, the Dahl, and the LuGre [ 20 ] model; neural-network models, such as the back propagation neural network based model [ 21 ], the gated recurrent unit based model [ 22 ], the neural network adaptive control method [ 23 ] and etc; operator-based models, such as the Preisach [ 24 ] model, the Krasnosel’skii–Pokrovskii(KP) [ 25 ] model, the Maxwell-slip [ 26 ] model, the Prandtl–Ishlinskii(PI) [ 27 ] model, and their variations.…”

Section: Introductionmentioning

confidence: 99%

A Digitized Representation of the Modified Prandtl–Ishlinskii Hysteresis Model for Modeling and Compensating Piezoelectric Actuator Hysteresis

Zhou

Feng

Aye³

et al. 2021

Micromachines

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Piezoelectric actuators are widely used in micromanipulation and miniature robots due to their rapid response and high repeatability. The piezoelectric actuators often have undesired hysteresis. The Prandtl–Ishlinskii (PI) hysteresis model is one of the most popular models for modeling and compensating the hysteresis behaviour. This paper presents an alternative digitized representation of the modified Prandtl–Ishlinskii with the dead-zone operators (MPI) hysteresis model to describe the asymmetric hysteresis behavior of piezoelectric actuators. Using a binary number with n digits to represent the classical Prandtl–Ishlinskii hysteresis model with n elementary operators, the inverse model can be easily constructed. A similar representation of the dead-zone operators is also described. With the proposed digitized representation, the model is more intuitive and the inversion calculation is avoided. An experiment with a piezoelectric stacked linear actuator is conducted to validate the proposed digitized MPI hysteresis model and it is shown that it has almost the same performance as compared to the classical representation.

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“…Many controllers were proposed and applied in the hysteresis compensation of PEAs during the past decade [14][15][16][17][18]. In [19], a neural network adaptive control is proposed without constructing the inversion of the hysteresis model. Compared with feedforward control, the frequency range of feedback control can be significantly improved [20].…”

Section: Introductionmentioning

confidence: 99%

High-Bandwidth Hysteresis Compensation of Piezoelectric Actuators via Multilayer Feedforward Neural Network Based Inverse Hysteresis Modeling

et al. 2021

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This paper proposes a feedforward and feedback combined hysteresis compensation method for a piezoelectric actuator (PEA) based on the multi-layer feedforward neural network (MFNN) inverse model. Under the scheme of direct inverse modeling, the MFNN is utilized as the feedforward hysteresis compensator, which can be directly identified from the measurements. The high modeling accuracy and high robustness of the MFNN help to increase the bandwidth of the closed-loop system. Experiments are conducted on a commercial PEA so as to verify the effectiveness of the proposed method. The superimposition of two sinusoidal signals is found to be efficient for the training of the MFNN. Closed-loop trajectory tracking experiments demonstrate that the bandwidth can be increased up to 1000 Hz and the maximum deviation can be maintained closed to the noise level. Meanwhile, there are only two parameters to be tuned in the proposed method, which guarantees ease of use for the inexperienced users. The proposed method successfully realizes high-precision hysteresis compensation performance across a wider frequency range.

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Neural network adaptive control of nonlinear systems preceded by hysteresis

Cited by 4 publications

References 44 publications

Neural network based predictive control with optimized search space for dynamic tracking of a piezo-actuated nano stage

Neural network based predictive control with optimized search space for dynamic tracking of a piezo-actuated nano stage

A Digitized Representation of the Modified Prandtl–Ishlinskii Hysteresis Model for Modeling and Compensating Piezoelectric Actuator Hysteresis

High-Bandwidth Hysteresis Compensation of Piezoelectric Actuators via Multilayer Feedforward Neural Network Based Inverse Hysteresis Modeling

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