Reset moving horizon estimation for quantized discrete time systems
This work addresses reset moving horizon estimation for multiple output discrete-time systems with quantized measurements. A new state reset estimator is designed based on a one-dimension noisy measurement to overcome underestimation or over-estimation of the system state, and an iterative algorithm...
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sg-ntu-dr.10356-1594912022-06-24T00:52:08Z Reset moving horizon estimation for quantized discrete time systems Xu, Yong Zhou, Jiayu Rao, Hongxia Lu, Renquan Xie, Lihua School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Estimator State Reset Moving Horizon This work addresses reset moving horizon estimation for multiple output discrete-time systems with quantized measurements. A new state reset estimator is designed based on a one-dimension noisy measurement to overcome underestimation or over-estimation of the system state, and an iterative algorithm is proposed to deal with multiple output systems. It is shown that with the proposed reset algorithm, the state estimation error is improved in the presence of over or under estimation, and the boundedness of the estimation error is established. The proposed algorithm also achieves a better estimate than the existing one for systems with a scalar measurement in the static case. A simulation of a moving vehicle is provided to demonstrate the advantage of the developed approach. This work was supported in part by the National Natural Science Foundation of China under Grant 61876041 and Grant 61973123, in part by the Local Innovative and Research Teams Project of Guangdong Special Support Program under Grant 2019BT02X353, in part by the Science and Technology Program of Guangzhou under Grant 201904020006, and in part by Guangdong Province Higher Vocational Colleges and Schools Pearl River Young Scholar approved in 2017. 2022-06-24T00:52:08Z 2022-06-24T00:52:08Z 2020 Journal Article Xu, Y., Zhou, J., Rao, H., Lu, R. & Xie, L. (2020). Reset moving horizon estimation for quantized discrete time systems. IEEE Transactions On Automatic Control, 66(9), 4199-4205. https://dx.doi.org/10.1109/TAC.2020.3037140 0018-9286 https://hdl.handle.net/10356/159491 10.1109/TAC.2020.3037140 2-s2.0-85098748608 9 66 4199 4205 en IEEE Transactions on Automatic Control © 2020 IEEE. All rights reserved. |
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Engineering::Electrical and electronic engineering Estimator State Reset Moving Horizon Xu, Yong Zhou, Jiayu Rao, Hongxia Lu, Renquan Xie, Lihua Reset moving horizon estimation for quantized discrete time systems |
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This work addresses reset moving horizon estimation for multiple output discrete-time systems with quantized measurements. A new state reset estimator is designed based on a one-dimension noisy measurement to overcome underestimation or over-estimation of the system state, and an iterative algorithm is proposed to deal with multiple output systems. It is shown that with the proposed reset algorithm, the state estimation error is improved in the presence of over or under estimation, and the boundedness of the estimation error is established. The proposed algorithm also achieves a better estimate than the existing one for systems with a scalar measurement in the static case. A simulation of a moving vehicle is provided to demonstrate the advantage of the developed approach. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Xu, Yong Zhou, Jiayu Rao, Hongxia Lu, Renquan Xie, Lihua |
format |
Article |
author |
Xu, Yong Zhou, Jiayu Rao, Hongxia Lu, Renquan Xie, Lihua |
author_sort |
Xu, Yong |
title |
Reset moving horizon estimation for quantized discrete time systems |
title_short |
Reset moving horizon estimation for quantized discrete time systems |
title_full |
Reset moving horizon estimation for quantized discrete time systems |
title_fullStr |
Reset moving horizon estimation for quantized discrete time systems |
title_full_unstemmed |
Reset moving horizon estimation for quantized discrete time systems |
title_sort |
reset moving horizon estimation for quantized discrete time systems |
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2022 |
url |
https://hdl.handle.net/10356/159491 |
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1736856416124141568 |