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

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 m...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science Journal of mechanical engineering science, 2021-12, Vol.235 (24), p.7836-7847
Main Authors: Son, Nguyen Ngoc, Anh, Ho Pham Huy
Format: Article
Language:English
Subjects:
Algorithms
Back propagation networks
Compensation
Compensators
Evolutionary computation
Feedback control
Feedforward control
Heuristic methods
Hysteresis models
Neural networks
Nonlinearity
Particle swarm optimization
Performance evaluation
Piezoelectric actuators
Tracking control
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Summary: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.
ISSN:0954-4062
2041-2983
DOI:10.1177/09544062211012718