摘要
提出一种基于特征模型的强跟踪无迹卡尔曼滤波(CSUKF)算法对状态和参数进行联合估计,利用特征模型参数构造时变的二阶状态转移阵,使滤波和辨识模型简化;结合强跟踪滤波(STF)的强跟踪能力和无迹卡尔曼滤波的(UKF)的非线性高逼近性对含测量噪声的高超声速飞行器系统进行参数辨识和滤波,并将其与非线性黄金分割自适应控制律相结合,对高超声速飞行器进行姿态控制.最后,将提出的CSUKF与基于特征模型的无迹卡尔曼滤波(CUKF)和基于特征模型的普通扩展卡尔曼滤波算法(CEKF)进行比较,仿真结果说明CSUKF与非线性黄金分割自适应控制律相结合可以有效改善控制的平稳性,且具有更好的滤波精度和系统输出,从而能更好地处理含测量噪声情况下的高超声速飞行器的辨识与控制问题.
A new adaptive filter is proposed combining characteristic model with STF and UKF.The filtering and identifying algorithm is greatly simplified by constructing a time-varying two-order state transfer matrix by use of the characteristic parameters.The filter together with the nonlinear golden section control law is applied for the attitude control of the hypersonic vehicle with measurement noise.Finally,the proposed strategy is compared with CUKF and CEKF.Numerical simulations have demonstrated that the combination of CSUKF and nonlinear golden section control law has better performance and offers a better solution to identification and control of hypersonic vehicle with measurement noise.
出处
《空间控制技术与应用》
2011年第3期14-20,共7页
Aerospace Control and Application
基金
国家自然科学基金资助项目(60736023)
