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基于UKF的移动机器人主动建模及模型自适应控制方法
引用本文:宋崎,韩建达.基于UKF的移动机器人主动建模及模型自适应控制方法[J].机器人,2005,27(3):226-230.
作者姓名:宋崎  韩建达
作者单位:1. 中国科学院沈阳自动化研究所,辽宁,沈阳,110016;中国科学院研究生院,北京,100039
2. 中国科学院沈阳自动化研究所,辽宁,沈阳,110016
摘    要:利用基于无色卡尔曼滤波(Unscented Kalman Filter, UKF)的状态和参数联合估计方法对移动机器人进行在线主动建模,基于该主动模型的逆动力学控制方法,实现了移动机器人对其自身不确定因素的自主性. 在针对全方位移动机器人的仿真实验中,验证了UKF对时变的状态和参数的收敛性和跟踪能力,并给出了不确定界. 基于主动建模的逆动力学控制方法与常值PID控制方法的比较结果,验证了该方法的有效性.

关 键 词:UKF  主动建模  在线  联合估计  逆动力学控制
文章编号:1002-0446(2005)03-0226-05
收稿时间:2004-09-30

UKF-based Active Modeling and Model-reference Adaptive Control for Mobile Robots
SONG Qi,HAN Jian-da.UKF-based Active Modeling and Model-reference Adaptive Control for Mobile Robots[J].Robot,2005,27(3):226-230.
Authors:SONG Qi  HAN Jian-da
Affiliation:SONG Qi~1,2,HAN Jian-da1
Abstract:The Unscented Kalman Filter (UKF) is employed to build an online model for mobile robots by means of joint estimation of states and parameters. Based on this active model, the inverse dynamic control (IDC) is further proposed to make the robot autonomously adaptive to its internal uncertainties. Extensive simulations are conducted with respect to the dynamics of an omni-directional mobile robot. The convergence and tracking ability as well as the uncertainty bound of UKF to estimate time-varying states and parameters are presented. Results of the IDC enhanced by active estimation are also compared with those of a classic PD control with constant gains to demonstrate the effectiveness of the control scheme.
Keywords:UKF  active modeling  online  joint estimation  inverse dynamic control
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