Minimum Entropy Fault-Tolerant Control of the Non-Gaussian Stochastic Distribution System

Abstract

Stochastic distribution control (SDC) systems are a group of systems where the outputs considered are the measured probability density functions (PDFs) of the system output while subjected to a normal crisp input. The linear B-spline model is used for the shape control of the system output probability density functions (PDFs). If target PDF cannot be determined in advance, entropy is first introduced to design the fault tolerant controller of SDC systems. The mean represents the center location of the stochastic variable; it is reasonable that minimum entropy fault tolerant controller can be designed subjected to mean constraints. Through the controller reconfiguration, the system entropy subjected to mean restriction can still be minimized when fault occurs. An illustrative example is utilized to demonstrate the use of fault diagnosis and minimum entropy fault tolerant control algorithms.