Pires Danubia S, Serra Ginalber L O
Federal Institute of Education, Sciences and Technology, São Luis MA, Brazil.
ISA Trans. 2020 Oct;105:1-23. doi: 10.1016/j.isatra.2020.05.034. Epub 2020 May 29.
A methodology for evolving fuzzy Kalman filter identification, is proposed in this paper. The mathematical formulation contemplates the following aspects: for initial estimation, an offline GK clustering algorithm and an offline fuzzy version of OKID algorithm are used to estimate antecedent and consequent parameters, respectively. From each new sample of input-output experimental data from dynamical system, the evolving eTS algorithm and an evolving fuzzy version of OKID algorithm are used to estimate the antecedent and consequent parameters of the evolving fuzzy Kalman filter, respectively. Computational and experimental results considering the estimation of states and outputs of a nonlinear dynamic system and a 2DoF helicopter, respectively, show the efficiency and applicability of the proposed methodology.
本文提出了一种用于进化模糊卡尔曼滤波器辨识的方法。数学公式考虑了以下几个方面：对于初始估计，使用离线GK聚类算法和离线模糊版本的OKID算法分别估计前件和后件参数。从动态系统输入-输出实验数据的每个新样本中，分别使用进化eTS算法和进化模糊版本的OKID算法来估计进化模糊卡尔曼滤波器的前件和后件参数。分别考虑非线性动态系统和二自由度直升机状态及输出估计的计算和实验结果，表明了所提方法的有效性和适用性。
