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Towards a theoretical foundation of PID control for uncertain nonlinear systems 期刊论文  OAI收割
AUTOMATICA, 2022, 卷号: 142, 页码: 12
作者:  
Zhao, Cheng;  Guo, Lei
  |  收藏  |  浏览/下载:20/0  |  提交时间:2023/02/07
PID Control of Planar Nonlinear Uncertain Systems in the Presence of Actuator Saturation 期刊论文  OAI收割
IEEE/CAA Journal of Automatica Sinica, 2022, 卷号: 9, 期号: 1, 页码: 90-98
作者:  
Xujun Lyu;  Zongli Lin
  |  收藏  |  浏览/下载:59/0  |  提交时间:2021/11/03
Control of Nonlinear Uncertain Systems by Extended PID 期刊论文  OAI收割
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2021, 卷号: 66, 期号: 8, 页码: 3840-3847
作者:  
Zhao, Cheng;  Guo, Lei
  |  收藏  |  浏览/下载:26/0  |  提交时间:2021/10/26
A parameter formula connecting PID and ADRC 期刊论文  OAI收割
SCIENCE CHINA-INFORMATION SCIENCES, 2020, 卷号: 63, 期号: 9, 页码: 13
作者:  
Zhong, Sheng;  Huang, Yi;  Guo, Lei
  |  收藏  |  浏览/下载:29/0  |  提交时间:2020/09/23
Feedback and uncertainty: Some basic problems and results 期刊论文  OAI收割
ANNUAL REVIEWS IN CONTROL, 2020, 卷号: 49, 页码: 27-36
作者:  
Guo, Lei
  |  收藏  |  浏览/下载:21/0  |  提交时间:2021/01/14
Uncoupled PID Control of Coupled Multi-Agent Nonlinear Uncertain systems 期刊论文  OAI收割
JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY, 2018, 卷号: 31, 期号: 1, 页码: 4-21
作者:  
Yuan, Shuo;  Zhao, Cheng;  Guo, Lei
  |  收藏  |  浏览/下载:26/0  |  提交时间:2018/07/30
PID controller design for second order nonlinear uncertain systems 期刊论文  OAI收割
SCIENCE CHINA-INFORMATION SCIENCES, 2017, 卷号: 60, 期号: 2, 页码: 13
作者:  
Zhao, Cheng;  Guo, Lei
  |  收藏  |  浏览/下载:21/0  |  提交时间:2018/07/30
Application of Combined Controller Based on CMAC and Nonlinear PID in Dual Redundant Telescope Tracking System 会议论文  OAI收割
Montréal, Quebec, Canada, 2014-6-22
作者:  
Wujun;  RenChangzhi;  LiHeng
收藏  |  浏览/下载:24/0  |  提交时间:2015/03/12
Double inverted pendulum control based on three-loop PID and improved BP neural network (EI CONFERENCE) 会议论文  OAI收割
2011 2nd International Conference on Digital Manufacturing and Automation, ICDMA 2011, August 5, 2011 - August 7, 2011, Zhangjiajie, Hunan, China
作者:  
Fan Y.
收藏  |  浏览/下载:37/0  |  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.  
Nonlinear control design for system with mechanical resonance based on two tracking-differentiators (EI CONFERENCE) 会议论文  OAI收割
2010 IEEE International Conference on Mechatronics and Automation, ICMA 2010, August 4, 2010 - August 7, 2010, Xi'an, China
作者:  
Li J.;  Li Y.;  Li Y.;  Li Y.;  Li Y.
收藏  |  浏览/下载:22/0  |  提交时间:2013/03/25
A nonlinear control strategy for controlling a motion system with mechanical resonance is presented in this paper. A mathematical model of the servo system is derived according to the parameters of an actual system. By using two Tracking-Differentiators  reference input and system output are tracked and their differential signals are given simultaneously. A nonlinear PD control law in velocity loop is designed based on non-smooth feedback. The simulation results show that the control law is superior remarkably in performance to linear PD control law and the classical PID control law. Tracking-Differentiator resolves the contradiction between rapidity and overshoot of system response. The nonlinear PD control law improves system dynamic performance and intensifies the ability of disturbance rejection. The control design achieved in this paper makes the servo system more robust. 2010 IEEE.