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An Active Disturbance Rejection Controller with Hysteresis Compensation for Piezoelectric Actuators 会议论文
2016 12th World Congress on Intelligent Control and Automation, 中国桂林, June 12-15, 2016
作者:  Liu WC(刘伟川);  Cheng L(程龙);  Hou ZG(侯增广);  Tan M(谭民);  long cheng
浏览  |  Adobe PDF(1692Kb)  |  收藏  |  浏览/下载：361/143  |  提交时间：2016/06/22
Hysteresis Adrc Tracking Control  
An Inversion-Free Predictive Controller for Piezoelectric Actuators Based on a Dynamic Linearized Neural Network Model 期刊论文
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2016, 卷号: 21, 期号: 1, 页码: 214-226
作者:  Liu, Weichuan;  Cheng, Long;  Hou, ZengGuan;  Yu, Junzhi;  Tan, Min
Adobe PDF(4915Kb)  |  收藏  |  浏览/下载：459/147  |  提交时间：2016/06/14
Dynamic Linearization  Hysteresis  Model Predictive Control (Mpc)  Neural Network Modeling  Piezoelectric Actuators  
Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2016, 卷号: 27, 期号: 2, 页码: 322-333
作者:  Wang, Yunpeng;  Cheng, Long;  Hou, ZengGuang;  Yu, Junzhi;  Tan, Min
Adobe PDF(2069Kb)  |  收藏  |  浏览/下载：397/105  |  提交时间：2016/06/14
Combinational Optimization Problem  Multirobot System  Optimal Formation  Recurrent Neural Network  Shape Theory  
Reaching a consensus in networks of high-order integral agents under switching directed topologies 期刊论文
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2016, 卷号: 47, 期号: 8, 页码: 1966-1981
作者:  Cheng, Long;  Wang, Hanlei;  Hou, Zeng-Guang;  Tan, Min
收藏  |  浏览/下载：273/0  |  提交时间：2016/03/19
Multi-agent Systems  High-order Integral Agent  Linear Dynamics  Switching Directed Topology  Consensus  
Reaching a stochastic consensus in the noisy networks of linear MIMO agents: Dynamic output-feedback and convergence rate 期刊论文
SCIENCE CHINA-TECHNOLOGICAL SCIENCES, 2016, 卷号: 59, 期号: 1, 页码: 45-54
作者:  Wang YunPeng;  Cheng Long;  Yang ChenGuang;  Hou ZengGuang;  Tan Min
浏览  |  Adobe PDF(995Kb)  |  收藏  |  浏览/下载：324/59  |  提交时间：2016/03/19
Multi-agent System  Mean Square Consensus  Communication Noise  Noise-attenuation Gain  Convergence Rate