摘要
针对离散非线性系统,提出一种基于T-S模糊模型的广义预测控制方法。该方法将采样点的T-S模糊模型转化为采样点线性模型与非线性误差叠加的线性形式,通过迭代修正非线性误差,使具有非线性误差的线性模型预测控制律逐渐逼近采样点T-S模糊模型预测控制律。同时,该预测控制方法也能适用于当系统受输入输出约束时的控制。仿真结果验证了所提出的TS模糊模型广义预测方法有效。
For discrete nonlinear systems, a new T - S fuzzy model based generalized predictive con- trol approach is proposed. In this method, the T -S fuzzy model at sampling points is converted into a superposition linear form of the linear model at sample point and nonlinear error. The predictive control law of T - S fuzzy model at sampling points is gradually approached by the predictive control law of linear model at sample point through iteratively correcting nonlinear error which is the difference between the nonlinear model and linear model. Meanwhile, the method can be applied to the system which is con- strained for the input and output. Simulation results show that the T - S fuzzy model based generalized predictive control approach in this paper is effective.
出处
《工业仪表与自动化装置》
2016年第6期3-7,共5页
Industrial Instrumentation & Automation
基金
国家自然科学基金(61461046)
关键词
T-S模糊模型
广义预测控制
采样点线性模型
非线性误差
迭代修正
T - S fuzzy model
generalized predictive control
sampling points linear model
nonlin-ear error
iterative correction
