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Double Inverted Pendulum Based on LQG Optimal Control 会议论文
International Conference on Automatic Control and Information Engineering (ICACIE), OCT 22-23, 2016
作者:  Zheng Yi;  Zhong Peisi;  Yue Qing Chao
收藏  |  浏览/下载：2/0  |  提交时间：2019/12/31
旋转二级倒立摆摆起倒立混合控制 Hybrid Swing-up and Handstand Control of Rotary Double Inverted Pendulum 期刊论文
2015, 卷号: 37, 页码: 550-555
作者:  但远宏[1,2];  徐鹏[2];  谭智[1,2];  李祖枢[1,2]
收藏  |  浏览/下载：3/0  |  提交时间：2019/11/29
MBPOA-based LQR controller and its application to the double-parallel inverted pendulum system 期刊论文
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2014, 卷号: 36, 页码: 262-268
作者:  Wang, Ling[1];  Ni, Haoqi[2];  Zhou, Weifeng[3];  Pardalos, Panos M.[4];  Fang, Jiating[5]
收藏  |  浏览/下载：13/0  |  提交时间：2019/04/30
二级倒立摆DU2UD的非线性控制研究 Research on Nonlinear Control of Double Inverted Pendulum DU2UD 期刊论文
2013, 卷号: 32, 页码: 53-56
作者:  但远宏[1,2];  李祖枢[1]
收藏  |  浏览/下载：4/0  |  提交时间：2019/11/28
Design and Research of Double Closed-Loop Control Strategy for Inverted Pendulum System 会议论文
2013 THIRD INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEM DESIGN AND ENGINEERING APPLICATIONS (ISDEA), 2013-01-01
作者:  Ru Feifei[1];  Zhang Lei[2];  Tang Le[3];  Huang Yanhai[4];  Zhang Pengpeng[5]
收藏  |  浏览/下载：6/0  |  提交时间：2019/12/23
一种改进的二级倒立摆LQR控制器参数优化方法 An Improved Method For Rectilinear Double Inverted Pendulum LQR Controller Parameter Optimization 期刊论文
2012, 卷号: 26, 页码: 85-88
作者:  谭文龙[1]
收藏  |  浏览/下载：1/0  |  提交时间：2019/11/28
二级倒立摆UD2UU的仿人智能控制分析 Research of UD2UU with human simulated intelligent control for a double inverted pendulum 期刊论文
2012, 卷号: 35, 页码: 134-140
作者:  但远宏[1,2];  李祖枢[1,2];  张小川[2];  谭智[1,2]
收藏  |  浏览/下载：3/0  |  提交时间：2019/11/29
Research of UD2UU with human simulated intelligent control for a double inverted pendulum 期刊论文
2012, 卷号: 35, 页码: 134-140
作者:  Dan, Yuan-Hong[1,2];  Li, Zu-Shu[1,2];  Zhang, Xiao-Chuan[2];  Tan, Zhi[1,2]
收藏  |  浏览/下载：5/0  |  提交时间：2019/11/29
Double inverted pendulum control based on three-loop PID and improved BP neural network (EI CONFERENCE) 会议论文
2011 2nd International Conference on Digital Manufacturing and Automation, ICDMA 2011, August 5, 2011 - August 7, 2011, Zhangjiajie, Hunan, China
Sang Y.; Fan Y.; Liu B.
收藏  |  浏览/下载：28/0  |  提交时间：2013/03/25
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 acquried 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. 2011 IEEE.  
A fuzzy control method based on information integration for double inverted pendulum (EI CONFERENCE) 会议论文
2011 2nd International Conference on Digital Manufacturing and Automation, ICDMA 2011, August 5, 2011 - August 7, 2011, Zhangjiajie, Hunan, China
Fan Y.; Sang Y.; Liu B.
收藏  |  浏览/下载：52/0  |  提交时间：2013/03/25
This article proposes a new fuzzy controller based on information integration. The mathematical model of Linear double inverted pendulum has been studied and estabLished with dynamics analytical method and LQR theory is used to design the optimal Linear inverted pendulum controller  then  the integration technology is used to design the variable parameters self-tuning fuzzy controller. Thereby  the fuzzy controller input variable dimension and the number of fuzzy control rules have been extremely reduced. Two controllers are designed for inverted pendulum system control and the comparison simulation experiments have been done. The results show that the controllers can both reaLize good control  and the fuzzy controller has higher precision  faster response  better stabiLity and robustness. 2011 IEEE.  

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