一种基于变论域自适应模糊逻辑系统提高卡尔曼滤波器性能的方法

Variable Universe Fuzzy System Based Method for Improving the Performance of Kalman Filter

实际工程应用中,预测动态系统的状态通常是一类比较重要的问题。卡尔曼滤波器已经被证明是处理该类问题的一个有效的工具。在系统特性能够较好把握的前提下,卡尔曼滤波器的预测准确性很大程度上取决于测量噪声协方差矩阵R的实时取值情况。但由于工作状况的各种不确定性,使得R矩阵的值会持续地受到不确定的干扰,从而极大地影响了卡尔曼滤波器的工作性能。针对上述问题,考虑到变论域自适应模糊逻辑系统的独特优点,提出了一种基于变论域自适应模糊逻辑系统动态实时调整R矩阵的方法。通过对滤波新息序列的实时监测,利用其实际值与理论值之间的偏差,采用变论域自适应模糊逻辑系统动态地对卡尔曼滤波器进行实时调整,通过仿真算例,验证了此种方法的优越性。应用此方法,在提高卡尔曼滤波器的预测精度的同时,也简化了模糊逻辑系统的设计,从而为更加方便、有效地应用卡尔曼滤波器提供了一种新的思路。

In practical applications, it is usually of great importance to predict the states of dynamical systems. Kalman filter has been proven to be an effective tool to solve these prediction problems. With a good understanding of the system characteristics, the prediction accuracy of Kalman Filter is more dependent on the real-time value of the measurement noise covariance matrix. However, because of the uncertainties of working conditions, the values in matrix are disturbed continuously. This case greatly affects the performance of Kalman filter. To solve the problem mentioned above, considering the special advantages of variable universe fuzzy system, a method based on variable universe fuzzy system is proposed to adjust the matrix dynamically in real time. Through monitoring the innovation sequence of Kalman filter, the deviation of the innovation value from the theoretical value is calculated. Based on deviation value, Kalman filter is then adjusted by the variable universe fuzzy system. The benefits of the mentioned method are verified by simulation. It improves the prediction accuracy of Kalman Filter, simplifies the design of fuzzy logic system, and therefore provides a new thinking for applying Kalman filter more conveniently and effectively.