机构

• 长春光学精密机械与物... [1]
• 数学与系统科学研究院 [1]
• 自动化研究所 [1]
• 沈阳自动化研究所 [1]

采集方式

• OAI收割 [4]

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浏览/检索结果: 共4条，第1-4条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Adaptive Containment Control for Fractional-Order Nonlinear Multi-Agent Systems With Time-Varying Parameters 期刊论文  OAI收割 IEEE/CAA Journal of Automatica Sinica, 2022, 卷号: 9, 期号: 9, 页码: 1627-1638 作者:  Yang Liu;  Huaguang Zhang;  Yingchun Wang;  Hongjing Liang   |  收藏  |  浏览/下载：27/0  |  提交时间：2022/08/19 Adaptive backstepping control  command filter  fractional-order multi-agent system  time-varying parameters   Identifying topologies and system parameters of uncertain time-varying delayed complex networks 期刊论文  OAI收割 SCIENCE CHINA-TECHNOLOGICAL SCIENCES, 2019, 卷号: 62, 期号: 1, 页码: 94-105 作者:  Wang Xiong;  Gu HaiBo;  Wang QianYao;  Lu JinHu   |  收藏  |  浏览/下载：47/0  |  提交时间：2019/04/02 system parameters and network topologies identification  anticipatory synchronization  uncertain time-varying delayed complex networks  noise-perturbed complex networks   Platform and steady kalman state observer design for intelligent vehicle based on visual guidance (EI CONFERENCE) 会议论文  OAI收割 2008 IEEE International Conference on Industrial Technology, IEEE ICIT 2008, April 21, 2008 - April 24, 2008, Chengdu, China Rong-hui Z.; Rong-ben W.; Feng Y.; Hong-guang J.; Tao C. 收藏  |  浏览/下载：21/0  |  提交时间：2013/03/25 State observer design is one of key technologies in research field of intelligent vehicle. Experiment platform  visual guidance intelligent vehicle JLUIV-5  is establishedby Jilin University Intelligent Vehicle Group firstly. The system structure and assistant navigation control system  and different image identify algorithms to recognize preview path and stops for variable illuminations are introduced. The dynamic response equation of steering control system was got by system identification experiment. By combined with the preview kinematics model  and two-degree steering dynamic model of vehicle  the steering kalman filter mathematics model based on preview kinematics for intelligent vehicle was obtained. And observer is designed by applying steady Kalman filter theory. The simulation and experiment results  carry out in Jilin University Nanling Campus and Culture Center of Jilin Province  show that the image identify algorithms  and steady Kalmanstate observer designed by the proposed method has good adaptability for time-varying and parameters uncertain  it can satisfy intelligent vehicle trace the path reliably during outdoor experiment. 2008 IEEE.   Enhanced LQR control for unmanned helicopter in hover 会议论文  OAI收割 1st International Symposium on Systems and Control in Aerospace and Astronautics, Harbin, China, January 19-21, 2006 作者:  Jiang Z(姜哲);  Han JD(韩建达);  Wang YC(王越超);  Song Q(宋崎) 收藏  |  浏览/下载：27/0  |  提交时间：2012/06/06 Real time adaptability is of central importance for the control of Unmanned Helicopter flying under different circumstances. In this paper  an active model is employed to handle the time varying uncertainties involved in the helicopter dynamics during flight. In the scheme  a normal LQR control designed from a simplified model at hovering is enhanced by means of Unscented-Kalman-Filter (UKF) based estimation  which tries to online capture the error between the simplified model and the full dynamics. This is intended to achieve adaptive performance without the need of adjusting the controller modes or parameters along with the changing dynamics of helicopter. Simulations with respect to a model helicopter are conducted to verify both the UKF-based estimation and the enhanced LQR control. Results are also demonstrated with the normal LQR control with the active model enhancement.