基于T-S模糊模型的非线性系统状态估计

现实世界中的动力系统大多是非线性的,而非线性系统的状态估计方法目前主要有扩展卡尔曼滤波,无味滤波,粒子滤波等,但它们对于非线性程度很高的系统滤波结果不是很理想。若用模糊规则模型去逼近非线性系统,基于规则理论去寻求状态的估计,则估计性能能够得到较大的改善。将模糊推理理论与Kalman滤波结合,用线性的方法逼近非线性模型,提出一种对非线性系统目标状态估计的新方法,给出了具体的处理过程,仿真实验支持理论结果

State Estimation of Nonlinear System based on T-S Fuzzy Model

State estimation methods for nonlinear systems mainly includes extended Kalman filter,unscented Kalman filter,particle filtering.Howerer,for highly nonlinear systems,if a model based on fuzzy rules is used to approximate it,and rule-based theories to seek the state estimates,the estimation performance would be much better.A numerical simulation with the proposed method is given in this paper.