A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays

Piezoelectric actuators are widely used in micro/nanoscale robotic manipulators. Due to its hysteresis and dynamic-related nonlinearity, accurate displacement tracking control of piezoelectric actuator is challenging. Besides, in some low-cost practical systems with low sampling rate, transmission d...

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Autores principales:	Zhangming Du, Chao Zhou, Zhiqiang Cao, Shuo Wang, Long Cheng, Min Tan
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Electronics TK7800-8360 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/442da6fa0a66446fbc3c29667ba1950f
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id	oai:doaj.org-article:442da6fa0a66446fbc3c29667ba1950f
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spelling	oai:doaj.org-article:442da6fa0a66446fbc3c29667ba1950f2021-12-02T03:04:52ZA neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays1729-881410.1177/17298814211057698https://doaj.org/article/442da6fa0a66446fbc3c29667ba1950f2021-11-01T00:00:00Zhttps://doi.org/10.1177/17298814211057698https://doaj.org/toc/1729-8814Piezoelectric actuators are widely used in micro/nanoscale robotic manipulators. Due to its hysteresis and dynamic-related nonlinearity, accurate displacement tracking control of piezoelectric actuator is challenging. Besides, in some low-cost practical systems with low sampling rate, transmission delay causes mismatches between feedback and real displacement, further increasing the challenge in tracking control. In this article, a neural network-based model predictive controller (MPC) is proposed for precise tracking control of piezoelectric actuator’s displacement in situation where feedback is slow and delayed. The prediction model is based on a nonlinear-autoregressive-moving-average-with-exogenous-inputs framework, which outputs entire prediction horizon of future displacement in a single time, and is fulfilled by a multilayer feedforward neural network. An extended Kalman filter-based estimation for displacement is introduced to relieve the influence of feedback delays so as to improve dynamic performance of the controller. Another neural network is trained to provide initial values for MPC to reduce computation costs and improve performance in dynamic tracking. In a series of tracking experiments, the effectiveness of proposed controller is verified.Zhangming DuChao ZhouZhiqiang CaoShuo WangLong ChengMin TanSAGE PublishingarticleElectronicsTK7800-8360Electronic computers. Computer scienceQA75.5-76.95ENInternational Journal of Advanced Robotic Systems, Vol 18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronics TK7800-8360 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronics TK7800-8360 Electronic computers. Computer science QA75.5-76.95 Zhangming Du Chao Zhou Zhiqiang Cao Shuo Wang Long Cheng Min Tan A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays
description	Piezoelectric actuators are widely used in micro/nanoscale robotic manipulators. Due to its hysteresis and dynamic-related nonlinearity, accurate displacement tracking control of piezoelectric actuator is challenging. Besides, in some low-cost practical systems with low sampling rate, transmission delay causes mismatches between feedback and real displacement, further increasing the challenge in tracking control. In this article, a neural network-based model predictive controller (MPC) is proposed for precise tracking control of piezoelectric actuator’s displacement in situation where feedback is slow and delayed. The prediction model is based on a nonlinear-autoregressive-moving-average-with-exogenous-inputs framework, which outputs entire prediction horizon of future displacement in a single time, and is fulfilled by a multilayer feedforward neural network. An extended Kalman filter-based estimation for displacement is introduced to relieve the influence of feedback delays so as to improve dynamic performance of the controller. Another neural network is trained to provide initial values for MPC to reduce computation costs and improve performance in dynamic tracking. In a series of tracking experiments, the effectiveness of proposed controller is verified.
format	article
author	Zhangming Du Chao Zhou Zhiqiang Cao Shuo Wang Long Cheng Min Tan
author_facet	Zhangming Du Chao Zhou Zhiqiang Cao Shuo Wang Long Cheng Min Tan
author_sort	Zhangming Du
title	A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays
title_short	A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays
title_full	A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays
title_fullStr	A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays
title_full_unstemmed	A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays
title_sort	neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/442da6fa0a66446fbc3c29667ba1950f
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A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays

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