Standard cubature Kalman filter (CKF) algorithm has some disadvantages in stochastic system control, such as low control accuracy and poor robustness. This paper proposes a stochastic system control method based on adaptive correction CKF algorithm. Firstly, a nonlinear time-varying discrete stochastic system model with stochastic disturbances is constructed. The control model is established by using the CKF algorithm, the covariance matrix of standard CKF is optimized by square root filter, the adaptive correction of error covariance matrix is realized by adding memory factor to the filter, and the disturbance factors in nonlinear time-varying discrete stochastic systems are eliminated by multistep feedback predictive control strategy, so as to improve the robustness of the algorithm. Simulation results show that the state estimation accuracy of the proposed adaptive cubature Kalman filter algorithm is better than that of the standard cubature Kalman filter algorithm, and the proposed adaptive correction CKF algorithm has good control accuracy and robustness in the UAV control test.

中文翻译：

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基于自适应校正CKF算法的随机系统控制优化

标准库尔曼卡尔曼滤波器（CKF）算法在随机系统控制中具有一些缺点，例如控制精度低和鲁棒性差。提出了一种基于自适应校正CKF算法的随机系统控制方法。首先，建立了具有随机扰动的非线性时变离散随机系统模型。利用CKF算法建立控制模型，通过平方根滤波器对标准CKF的协方差矩阵进行优化，通过在滤波器中添加存储因子实现对误差协方差矩阵的自适应校正，并在非线性时变中进行干扰因子的修正。多步反馈预测控制策略消除了离散随机系统，提高了算法的鲁棒性。