Multi-objective PID control for non-Gaussian stochastic distribution system based on two-step intelligent models  

作　　者：YI Yang ZHANG TianPing GUO Lei 

机构地区：[1]Department of Automation, College of Information Engineering, Yangzhou University, Yangzhou 225009, China [2]School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100083, China

出　　处：《Science in China(Series F)》2009年第10期1754-1765,共12页中国科学（F辑英文版）

基　　金：Supported by the National Natural Science Foundation of China (Grant Nos. 60774013, 60874045, 60904030)

摘　　要：A new method for controlling the shape of the conditional output probability density function （PDF） for general nonlinear dynamic stochastic systems is proposed based on B-spline neural network （NN） model and T-S fuzzy model. Applying NN approximation to the measured PDFs, we transform the concerned problem into the tracking of given weights. Meanwhile, the complex multi-delay T-S fuzzy model with exogenous disturbances, parametric uncertainties and state constraints is used to represent the nonlinear weight dynamics. Moreover, instead of the non-convex design algorithms and PI control, the improved convex linear matrix inequality （LMI） algorithms and the generalized PID controller are proposed such that the multiple control objectives including stability, robustness, tracking performance and state constraint can be guaranteed simultaneously. Simulations are performed to demonstrate the efficiency of the proposed approach.A new method for controlling the shape of the conditional output probability density function （PDF） for general nonlinear dynamic stochastic systems is proposed based on B-spline neural network （NN） model and T-S fuzzy model. Applying NN approximation to the measured PDFs, we transform the concerned problem into the tracking of given weights. Meanwhile, the complex multi-delay T-S fuzzy model with exogenous disturbances, parametric uncertainties and state constraints is used to represent the nonlinear weight dynamics. Moreover, instead of the non-convex design algorithms and PI control, the improved convex linear matrix inequality （LMI） algorithms and the generalized PID controller are proposed such that the multiple control objectives including stability, robustness, tracking performance and state constraint can be guaranteed simultaneously. Simulations are performed to demonstrate the efficiency of the proposed approach.

关 键 词：probability density function non-Gaussian stochastic systems PID controller B-spline NN L1 performance index T-S fuzzy model 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] TP277[自动化与计算机技术—控制科学与工程]