摘要
该文对非线性系统的建模采用Cao -Ress(C -R)模糊模型 ,并用卡尔曼滤波算法在线辨识模糊模型的结论参数 ,从而减少了参数辨识的数量和避免了矩阵的求逆运算 ,然后在每一个采样点对该系统进行局部动态线性化 ,根据得到的系统线性化模型对系统采取广义预测控制 (GPC)方法得到当前的控制动作。
A Cao-Ress(C-R) fuzzy model is constructed for nonlinear system, and the consequence parameters identification is obtained on-line by using Kalman filtering algorithm, so the number of parameters' identification is reduced and the calculation of matrix inversion is avoided. Local dynamic linearization is applied to the system at each sampling point. Then control action is gained using generalized predictive control(GPC) based on the linearized model. Its effectiveness for nonlinear systems is verified via simulation study.
出处
《计算机仿真》
CSCD
2004年第11期80-81,共2页
Computer Simulation
关键词
模糊模型
广义预测控制
非线性系统
卡尔曼滤波算法
Fuzzy model
Generalized predictive control
Nonlinear system
Kalman filtering algorithm
