摘要
给出了连续系统的一种广义线性模型,不仅适用于已知系统,而且适用于未知系统,无论系统是线性还是非线性的。该模型系数服从确定的变化规律而无需在线辨识或预先计算,因而实时性好。定义了基于这种模型的变参数广义卡尔曼滤波,并给出其递推计算公式,可对未知系统实现实时滤波估计,且满足均方误差最小。该系统能方便、快捷、准确地实现未来多个时刻状态群的实时预报,以便提早掌握系统未来的运动趋势,提高系统的控制性能和对机动目标拦截的制导精度。算例仿真表明,结果是令人满意的。
A generalized linear model is given, for systems containing linearity and nonlinearity, which may be used for beth known and unknown continuous systems. The coefficients of the models are fixed without distinguished on-line or predefined, therefore, the real-time of the models is good. The generalized Karlman filter is also defined with this model, and the recurrence formulas are provided. The real-time estimation for the unknown system is completed with the minimum mean-square error. Because the groups of the states in the future is estimated convenienfly, rapidly and accurately, so the move-tension of the systems in the future can be grasped early and beth the function of the control systems and the guidance-systems-accuracy are improved.
出处
《控制工程》
CSCD
2007年第1期21-23,共3页
Control Engineering of China
关键词
未知系统
机动目标
广义线性模型
广义卡尔曼滤波
变参数系统
unknown system
mobile objective
generalized linear model
generalized Kalman filter
varying parameter system
