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

查看全文 作　　者：YI [1]Yang;ZHANG [1]TianPing;GUO [2]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)》索引2009年第52卷第10期,共12页

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. 关键词：PID控制器智能模型随机系统分布系统 B样条神经网络非高斯多目标线性矩阵不等式

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

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