Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller Based on Long Short-Term Memory Neural Network

Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller Based on Long Short-Term Memory Neural Network

Model-free adaptive controller (MFAC) is a novel data-driven control methodology that relies only on input/output (I/O) measurement data instead of classic mathematical models of actual controlled plants. The single-input single-output (SISO) compact-form MFAC (SISO-CFMFAC) is a promising method for...

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Main Authors: Ye Yang, Chen Chen, Jiangang Lu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
LSTM neural network
model-free adaptive controller
parameter self-tuning
three-tank system
Online Access:https://ieeexplore.ieee.org/document/9170629/
id doaj-b687f751a201468289a02b17dbb8c882
record_format Article
spelling doaj-b687f751a201468289a02b17dbb8c8822021-03-30T04:47:12ZengIEEEIEEE Access2169-35362020-01-01815192615193710.1109/ACCESS.2020.30175329170629Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller Based on Long Short-Term Memory Neural NetworkYe Yang0https://orcid.org/0000-0002-1165-1314Chen Chen1https://orcid.org/0000-0002-2595-7816Jiangang Lu2https://orcid.org/0000-0002-1551-6179State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, ChinaModel-free adaptive controller (MFAC) is a novel data-driven control methodology that relies only on input/output (I/O) measurement data instead of classic mathematical models of actual controlled plants. The single-input single-output (SISO) compact-form MFAC (SISO-CFMFAC) is a promising method for controlling SISO nonlinear time-varying systems. The parameters in SISO-CFMFAC must be carefully tuned before use, as inappropriate parameters may lead to poor control performance. However, up to now, parameter tuning has been a time-consuming and laborious task. In this paper, a new approach called SISO-CFMFAC-LSTM is proposed for parameter self-tuning of SISO-CFMFAC based on long short-term memory (LSTM) neural network. To evaluate the performance of the proposed methodology, qualitative and quantitative comparisons with other existing control algorithms are carried out. Six individual performance indices, namely, the root mean square error (RMSE), the integral absolute error (IAE), the integral time-weighted absolute error (ITAE), the integral absolute variation of the control signal (IAVU), the maximum overshoot (MO), and the imprecise control ratio (ICR), are introduced for quantitative comparison. The experimental results demonstrate that the proposed SISO-CFMFAC-LSTM achieves the best performance in all indices, indicating that it is an effective control method for SISO nonlinear time-varying systems.https://ieeexplore.ieee.org/document/9170629/LSTM neural networkmodel-free adaptive controllerparameter self-tuningthree-tank system
collection DOAJ
language English
format Article
sources DOAJ
author Ye Yang
Chen Chen
Jiangang Lu
spellingShingle Ye Yang
Chen Chen
Jiangang Lu
Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller Based on Long Short-Term Memory Neural Network
IEEE Access
LSTM neural network
model-free adaptive controller
parameter self-tuning
three-tank system
author_facet Ye Yang
Chen Chen
Jiangang Lu
author_sort Ye Yang
title Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller Based on Long Short-Term Memory Neural Network
title_short Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller Based on Long Short-Term Memory Neural Network
title_full Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller Based on Long Short-Term Memory Neural Network
title_fullStr Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller Based on Long Short-Term Memory Neural Network
title_full_unstemmed Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller Based on Long Short-Term Memory Neural Network
title_sort parameter self-tuning of siso compact-form model-free adaptive controller based on long short-term memory neural network
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description Model-free adaptive controller (MFAC) is a novel data-driven control methodology that relies only on input/output (I/O) measurement data instead of classic mathematical models of actual controlled plants. The single-input single-output (SISO) compact-form MFAC (SISO-CFMFAC) is a promising method for controlling SISO nonlinear time-varying systems. The parameters in SISO-CFMFAC must be carefully tuned before use, as inappropriate parameters may lead to poor control performance. However, up to now, parameter tuning has been a time-consuming and laborious task. In this paper, a new approach called SISO-CFMFAC-LSTM is proposed for parameter self-tuning of SISO-CFMFAC based on long short-term memory (LSTM) neural network. To evaluate the performance of the proposed methodology, qualitative and quantitative comparisons with other existing control algorithms are carried out. Six individual performance indices, namely, the root mean square error (RMSE), the integral absolute error (IAE), the integral time-weighted absolute error (ITAE), the integral absolute variation of the control signal (IAVU), the maximum overshoot (MO), and the imprecise control ratio (ICR), are introduced for quantitative comparison. The experimental results demonstrate that the proposed SISO-CFMFAC-LSTM achieves the best performance in all indices, indicating that it is an effective control method for SISO nonlinear time-varying systems.
topic LSTM neural network
model-free adaptive controller
parameter self-tuning
three-tank system
url https://ieeexplore.ieee.org/document/9170629/
work_keys_str_mv AT yeyang parameterselftuningofsisocompactformmodelfreeadaptivecontrollerbasedonlongshorttermmemoryneuralnetwork
AT chenchen parameterselftuningofsisocompactformmodelfreeadaptivecontrollerbasedonlongshorttermmemoryneuralnetwork
AT jianganglu parameterselftuningofsisocompactformmodelfreeadaptivecontrollerbasedonlongshorttermmemoryneuralnetwork
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