Fault Tolerant Control for Nonlinear Singular Stochastic Distribution Systems Based on Fuzzy Modeling

Lina Yao,Lifan Li,Chunhui Lei

doi:10.1109/access.2019.2933562

Abstract

When the desired output probability density function (PDF) is unknown, the active fault-tolerant control (FTC) method for the non-Gaussian nonlinear singular stochastic distribution control (SDC) system is investigated in this paper. Algebraic constraints and the nonlinearity in singular systems make the design of fault diagnosis and fault-tolerant control more complex. Different from traditional static modeling methods, the linear fuzzy logic system is served for approximating the output PDF. Takagi-Sugeno (T-S) fuzzy model is used to describe the nonlinear system. Subsequently, a fuzzy descriptor fault diagnosis (FD) observer is used to provide the unknown fault information for the fault-tolerant controller design. Combining minimum entropy control and fault compensation algorithm, the minimum Shannon entropy fault tolerant control strategy is developed to compensate the performance losses caused by the fault. At last, simulation results are applied to demonstrate the effectiveness of the proposed algorithms.

Highlights

In some practical applications such as the pulp uniformity control in the process of paper-making, the particle uniformity control and the flame distribution control, the existence of nonlinear effects and non-Gaussian random variables will lead to non-Gaussian output
The complex nonlinear singular stochastic distribution control (SDC) system is described as T-S fuzzy model, which provides a new approach for fault diagnosis and fault tolerant control of the system
The following minimum Shannon entropy fault-tolerant controller is considered to compensate the performance losses caused by the fault uFTC = uS + uC

Summary

INTRODUCTION

In some practical applications such as the pulp uniformity control in the process of paper-making, the particle uniformity control and the flame distribution control, the existence of nonlinear effects and non-Gaussian random variables will lead to non-Gaussian output. The traditional control theory is no longer applicable to these systems To address this problem, the control theory of the shape of the output PDF is formulated [1], which is called the non-Gaussian SDC theory. L. Yao et al.: FTC for Nonlinear Singular Stochastic Distribution Systems Based on Fuzzy Modeling. A new method based on the entropy concept is proposed [23] for non-Gaussian twoinput and two-output dynamic stochastic systems. The nonlinear dynamics of the non-Gaussian singular SDC system is identified by using the T-S fuzzy model. 1) A novel method for approximating the output PDF based on the linear fuzzy logic system is proposed. The complex nonlinear singular SDC system is described as T-S fuzzy model, which provides a new approach for fault diagnosis and fault tolerant control of the system. 3) A minimum Shannon entropy controller is reconstructed to make the performance index be minimized when the objective PDF is unknown

MODEL DESCRIPTION

FAULT TOLERANT CONTROL DESIGN

CONCLUSION