论文部分内容阅读
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
Most dynamic systems in the real world are non-linear. However, the state estimation methods of non-linear systems mainly include Extended Kalman Filter, Odorless Filter and Particle Filter. However, they are not ideal for the non-linear system filtering results. If the fuzzy rule model is used to approximate a nonlinear system and the state estimation is sought based on the rule theory, the estimated performance can be greatly improved. Combining fuzzy reasoning theory and Kalman filter, a nonlinear method is approximated by a linear method. A new method for estimating the target state of a nonlinear system is proposed, and the specific process is given. The simulation results support the theoretical results