Double Inverted Pendulum Control Based on Three-loop PID and Improved BP Neural Network

To deal with the defects of BP neural networks used in balance control of inverted pendulum, such as longer train time and converging in partial minimum, this article realizes the control of double inverted pendulum with improved BP algorithm of artificial neural networks (ANN), builds up a training...

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Veröffentlicht in:2011 Second International Conference on Digital Manufacturing and Automation S. 456 - 459
Hauptverfasser: Yingjun Sang, Yuanyuan Fan, Bin Liu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2011
Schlagworte:
Algorithm design and analysis
Artificial neural networks
Control systems
double inverted pendulum
Educational institutions
improved back propagation algorithm
Mathematical model
Software algorithms
three-loop pid
Training
ISBN:1457707551, 9781457707551
Online-Zugang:Volltext
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Abstract To deal with the defects of BP neural networks used in balance control of inverted pendulum, such as longer train time and converging in partial minimum, this article realizes the control of double inverted pendulum with improved BP algorithm of artificial neural networks (ANN), builds up a training model of test simulation and the BP network is 6-10-1 structure. Tansig function is used in hidden layer and Purelin function is used in output layer, LM is used in training algorithm. The training data is acquired by three-loop PID algorithm. The model is learned and trained with Matlab calculating software, and the simulink simulation experiment results prove that improved BP algorithm for inverted pendulum control has higher precision, better astringency and lower calculation. This algorithm has wide application on nonlinear control and robust control field in particular.
AbstractList To deal with the defects of BP neural networks used in balance control of inverted pendulum, such as longer train time and converging in partial minimum, this article realizes the control of double inverted pendulum with improved BP algorithm of artificial neural networks (ANN), builds up a training model of test simulation and the BP network is 6-10-1 structure. Tansig function is used in hidden layer and Purelin function is used in output layer, LM is used in training algorithm. The training data is acquired by three-loop PID algorithm. The model is learned and trained with Matlab calculating software, and the simulink simulation experiment results prove that improved BP algorithm for inverted pendulum control has higher precision, better astringency and lower calculation. This algorithm has wide application on nonlinear control and robust control field in particular.
Author Yingjun Sang
Bin Liu
Yuanyuan Fan
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  surname: Yingjun Sang
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  surname: Yuanyuan Fan
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  email: fyuanyuan123@163.com
  organization: Fac. of Electron. & Electr. Eng., Huaiyin Inst. of Technol., Huaian, China
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  surname: Bin Liu
  fullname: Bin Liu
  organization: Fac. of Electron. & Electr. Eng., Huaiyin Inst. of Technol., Huaian, China
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Snippet To deal with the defects of BP neural networks used in balance control of inverted pendulum, such as longer train time and converging in partial minimum, this...
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StartPage 456
SubjectTerms Algorithm design and analysis
Artificial neural networks
Control systems
double inverted pendulum
Educational institutions
improved back propagation algorithm
Mathematical model
Software algorithms
three-loop pid
Training
Title Double Inverted Pendulum Control Based on Three-loop PID and Improved BP Neural Network
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