An Integrated Algorithm on Disturbance Rejection and Fault Diagnosis for A Class of Stochastic Distribution Systems
An integrated algorithm on system modeling, disturbance rejection and fault diagnosis problem is presented for a class of stochastic distribution control (SDC) systems with exogenous disturbance in this contribution. A 2-step neural network (NN) algorithm is employed to set up the plant. Unlike traditional SDC systems, the driven information is modeled in the first step as a kind of probability density function (PDF) of the output or its monitor information via a static neural network. An adatpive dynamic neural network is employed in the second step to identify the nonlinearity, uncertainty of the system, where the weight matrices of NN and their boundary are designed with adaptive rules. A full order disturbance observer is designed for disturbance rejection purpose while an adaptive 1st-order filter is designed for fault diagnosis purpose. Through such adaptive algorithm, nonlinear parameter estimation, disturbance and sensor fault identification can be well dealt with simultaneously. A sensor compensation rule is consequently given to restore the plant with output feedback controller. Simulation examples are given to verify the effectiveness of the presented algorithm.
Yumin Zhang Yunlong Liu Jia Liu
School of Instrumentation and Opto-Electronics Engineering, Beihang University, 100191 Beijing, Chin School of Instrumentation and Opto- Electronics Engineering, Beihang University, 100191 Beijing, Chi
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
5396-5400
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）