基于UKF的移动机器人主动建模及模型自适应控制方法  

UKF-based Active Modeling and Model-reference Adaptive Control for Mobile Robots

作　　者：宋崎[1] 韩建达[1] 

机构地区：[1]中国科学院沈阳自动化研究所

出　　处：《机器人》2005年第3期226-230,235,共6页Robot

摘　　要：利用基于无色卡尔曼滤波(UnscentedKalmanFilter,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.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.

关 键 词：UKF 主动建模 在线 联合估计 逆动力学控制 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]