An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this...

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Main Authors: Guojin Pei, Ming Yu, Yaohui Xu, Cui Ma, Houhu Lai, Fokui Chen, Hui Lin
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Applied Sciences
Subjects:
compliant force control
PID
neural network
Smith predictor
Online Access:https://www.mdpi.com/2076-3417/11/6/2685
id doaj-8b3b8510f44f46398932615b7f4f32fe
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spelling doaj-8b3b8510f44f46398932615b7f4f32fe2021-03-18T00:03:15ZengMDPI AGApplied Sciences2076-34172021-03-01112685268510.3390/app11062685An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith PredictorGuojin Pei0Ming Yu1Yaohui Xu2Cui Ma3Houhu Lai4Fokui Chen5Hui Lin6Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaA compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.https://www.mdpi.com/2076-3417/11/6/2685compliant force controlPIDneural networkSmith predictor
collection DOAJ
language English
format Article
sources DOAJ
author Guojin Pei
Ming Yu
Yaohui Xu
Cui Ma
Houhu Lai
Fokui Chen
Hui Lin
spellingShingle Guojin Pei
Ming Yu
Yaohui Xu
Cui Ma
Houhu Lai
Fokui Chen
Hui Lin
An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor
Applied Sciences
compliant force control
PID
neural network
Smith predictor
author_facet Guojin Pei
Ming Yu
Yaohui Xu
Cui Ma
Houhu Lai
Fokui Chen
Hui Lin
author_sort Guojin Pei
title An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor
title_short An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor
title_full An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor
title_fullStr An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor
title_full_unstemmed An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor
title_sort improved pid controller for the compliant constant-force actuator based on bp neural network and smith predictor
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2021-03-01
description A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.
topic compliant force control
PID
neural network
Smith predictor
url https://www.mdpi.com/2076-3417/11/6/2685
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