Elman neural network–based identification of rate-dependent hysteresis in piezoelectric actuators

Zhao, Xuelong; Shen, Shuai; Su, Liangcai; Yin, Xiuxing

doi:10.1177/1045389x20905987

Cited by 11 publications

(6 citation statements)

References 39 publications

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“…Some scholars used a recursive least squares algorithm to identify the system with time delay [ 7 , 8 , 9 ], which is assumed to be known and may not be established in engineering practice. Neural networks were used to identify time delay, which have the problem of long training times and easily falling into local optima [ 10 , 11 , 12 ]. Liu et al [ 13 ] developed a compressed sensing recovery algorithm for the multiple input single output finite impulse response systems with unknown time delay, but it was not easy to choose the optimal threshold.…”

Section: Introductionmentioning

confidence: 99%

Multi-Delay Identification of Rare Earth Extraction Process Based on Improved Time-Correlation Analysis

Liu

Yang

et al. 2023

Sensors

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The rare earth extraction process has significant time delay characteristics, making it challenging to identify the time delay and establish an accurate mathematical model. This paper proposes a multi-delay identification method based on improved time-correlation analysis. Firstly, the data are preprocessed by grey relational analysis, and the time delay sequence and time-correlation data matrix are constructed. The time-correlation analysis matrix is defined, and the H∞ norm quantifies the correlation degree of the data sequence. Thus the multi-delay identification problem is transformed into an integer optimization problem. Secondly, an improved discrete state transition algorithm is used for optimization to obtain multi-delay. Finally, based on an Neodymium (Nd) component content model constructed by a wavelet neural network, the performance of the proposed method is compared with the unimproved time delay identification method and the model without an identification method. The results show that the proposed algorithm improves optimization accuracy, convergence speed, and stability. The performance of the component content model after time delay identification is significantly improved using the proposed method, which verifies its effectiveness in the time delay identification of the rare earth extraction process.

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Section: Introductionmentioning

confidence: 99%

Multi-Delay Identification of Rare Earth Extraction Process Based on Improved Time-Correlation Analysis

Liu

Yang

et al. 2023

Sensors

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“…Recently some neural network based modeling methods are becoming popular due to their model identification properties. 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).…”

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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“…Sve cko et al 13 proposed a combined feed-forward neural network and BAT search algorithm to identify the hysteresis feature of the PZT mechanism model. Zhao et al 14 proposed an Elman neural network-based hysteresis model for the PZT actuator. Nematollahi et al 15 suggested a wavelet-based neural network for inverse identification of piezoelectric beam.…”

Section: Introductionmentioning

confidence: 99%

Hysteresis modelling and compensation for piezoelectric actuator using Jaya-BP neural network

Sơn

Ánh

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper proposes a new training algorithm using a hybrid Jaya-back propagation algorithm (called H-Jaya) to optimize the neural network weights, which is applied to identify the nonlinear hysteresis Piezoelectric actuator based on the experimental input-output data. The identified H-Jaya-neural model will be used to design an advanced feed-forward (FF) controller for compensating the hysteresis nonlinearity. Furthermore as to improve the tracking performance, a feed-forward-feedback control scheme is conducted. To evaluate the effectiveness of the proposed approach, firstly, it is tested through identifying the nonlinear hysteresis of Piezoelectric (PZT) actuator and compared with other meta-heuristic techniques, including differential evolution (DE), particle swarm optimization (PSO), and Jaya. Then, the accuracy of the hysteresis model-based compensator is evaluated under various control experiments using the piezoelectric actuator. The results of experiments executed on PZT actuator configured with a PZS001 from Thorlabs prove that the proposed approach obtains an excellent performance in hysteresis modeling and compensation.

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Elman neural network–based identification of rate-dependent hysteresis in piezoelectric actuators

Cited by 11 publications

References 39 publications

Multi-Delay Identification of Rare Earth Extraction Process Based on Improved Time-Correlation Analysis

Multi-Delay Identification of Rare Earth Extraction Process Based on Improved Time-Correlation Analysis

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

Hysteresis modelling and compensation for piezoelectric actuator using Jaya-BP neural network

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