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A Design of FPGA-Based Neural Network PID Controller for Motion Control System

In the actual industrial production process, the method of adaptively tuning proportional–integral–derivative (PID) parameters online by neural network can adapt to different characteristics of different controlled objects better than the controller with PID. However, the commonly used microcontroll...

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Autores principales: Wang, Jun, Li, Moudao, Jiang, Weibin, Huang, Yanwei, Lin, Ruiquan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8837942/
https://www.ncbi.nlm.nih.gov/pubmed/35161635
http://dx.doi.org/10.3390/s22030889
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author Wang, Jun
Li, Moudao
Jiang, Weibin
Huang, Yanwei
Lin, Ruiquan
author_facet Wang, Jun
Li, Moudao
Jiang, Weibin
Huang, Yanwei
Lin, Ruiquan
author_sort Wang, Jun
collection PubMed
description In the actual industrial production process, the method of adaptively tuning proportional–integral–derivative (PID) parameters online by neural network can adapt to different characteristics of different controlled objects better than the controller with PID. However, the commonly used microcontroller unit (MCU) cannot meet the application scenarios of real time and high reliability. Therefore, in this paper, a closed-loop motion control system based on BP neural network (BPNN) PID controller by using a Xilinx field programmable gate array (FPGA) solution is proposed. In the design of the controller, it is divided into several sub-modules according to the modular design idea. The forward propagation module is used to complete the forward propagation operation from the input layer to the output layer. The PID module implements the mapping of PID arithmetic to register transfer level (RTL) and is responsible for completing the output of control amount. The main state machine module generates enable signals that control the sequential execution of each sub-module. The error backpropagation and weight update module completes the update of the weights of each layer of the network. The peripheral modules of the control system are divided into two main parts. The speed measurement module completes the acquisition of the output pulse signal of the encoder and the measurement of the motor speed. The pulse width modulation (PWM) signal generation module generates PWM waves with different duty cycles to control the rotation speed of the motor. A co-simulation of Modelsim and Simulink is used to simulate and verify the system, and a test analysis is also performed on the development platform. The results show that the proposed system can realize the self-tuning of PID control parameters, and also has the characteristics of reliable performance, high real-time performance, and strong anti-interference. Compared with MCU, the convergence speed is far more than three orders of magnitude, which proves its superiority.
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spelling pubmed-88379422022-02-13 A Design of FPGA-Based Neural Network PID Controller for Motion Control System Wang, Jun Li, Moudao Jiang, Weibin Huang, Yanwei Lin, Ruiquan Sensors (Basel) Article In the actual industrial production process, the method of adaptively tuning proportional–integral–derivative (PID) parameters online by neural network can adapt to different characteristics of different controlled objects better than the controller with PID. However, the commonly used microcontroller unit (MCU) cannot meet the application scenarios of real time and high reliability. Therefore, in this paper, a closed-loop motion control system based on BP neural network (BPNN) PID controller by using a Xilinx field programmable gate array (FPGA) solution is proposed. In the design of the controller, it is divided into several sub-modules according to the modular design idea. The forward propagation module is used to complete the forward propagation operation from the input layer to the output layer. The PID module implements the mapping of PID arithmetic to register transfer level (RTL) and is responsible for completing the output of control amount. The main state machine module generates enable signals that control the sequential execution of each sub-module. The error backpropagation and weight update module completes the update of the weights of each layer of the network. The peripheral modules of the control system are divided into two main parts. The speed measurement module completes the acquisition of the output pulse signal of the encoder and the measurement of the motor speed. The pulse width modulation (PWM) signal generation module generates PWM waves with different duty cycles to control the rotation speed of the motor. A co-simulation of Modelsim and Simulink is used to simulate and verify the system, and a test analysis is also performed on the development platform. The results show that the proposed system can realize the self-tuning of PID control parameters, and also has the characteristics of reliable performance, high real-time performance, and strong anti-interference. Compared with MCU, the convergence speed is far more than three orders of magnitude, which proves its superiority. MDPI 2022-01-24 /pmc/articles/PMC8837942/ /pubmed/35161635 http://dx.doi.org/10.3390/s22030889 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Jun
Li, Moudao
Jiang, Weibin
Huang, Yanwei
Lin, Ruiquan
A Design of FPGA-Based Neural Network PID Controller for Motion Control System
title A Design of FPGA-Based Neural Network PID Controller for Motion Control System
title_full A Design of FPGA-Based Neural Network PID Controller for Motion Control System
title_fullStr A Design of FPGA-Based Neural Network PID Controller for Motion Control System
title_full_unstemmed A Design of FPGA-Based Neural Network PID Controller for Motion Control System
title_short A Design of FPGA-Based Neural Network PID Controller for Motion Control System
title_sort design of fpga-based neural network pid controller for motion control system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8837942/
https://www.ncbi.nlm.nih.gov/pubmed/35161635
http://dx.doi.org/10.3390/s22030889
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