Observer‐based actuator fault estimation and proportional derivative fault tolerant control for continuous‐time singular systems

Abstract This paper presents the problem of actuator fault estimation and fault-tolerant control (FTC) of a biological process using Takagi–Sugeno fuzzy formulation. The goal is to ensure the desired outputs tracking even if the time-varying actuator faults occur. We propose to use a proportional multi-integral (PMI) observer to estimate both the time-varying actuator faults and the state of system. The reconstructed faults are used to reconfigure the nominal controller. As a nominal control, we use a fuzzy linear quadratic integral (LQI) law. To ensure the global asymptotic convergence of the PMI observer and to improve the compensation speed of faults, we propose to use the multiple Lyapunov function by introducing a convergence rate. Sufficient conditions in terms of linear matrix inequalities (LMIs) are developed. The obtained results show that, the proposed approach is successfully applied to the problem of actuator fault-tolerant control of a bacterial growth process.

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Integrated fault estimation and non-fragile fault-tolerant control design for uncertain Takagi–Sugeno fuzzy systems with actuator fault and sensor fault

IET Control Theory and Applications ◽

10.1049/iet-cta.2016.1192 ◽

2017 ◽

Vol 11 (10) ◽

pp. 1542-1553 ◽

Cited By ~ 24

Author(s):

Xiaohang Li ◽

Dunke Lu ◽

Guohui Zeng ◽

Jin Liu ◽

Wei Zhang

Keyword(s):

Fuzzy Systems ◽

Fault Tolerant ◽

Control Design ◽

Fault Tolerant Control ◽

Fault Estimation ◽

Actuator Fault ◽

Sensor Fault ◽

Takagi Sugeno

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Actuator Fault Tolerant Control in Nonlinear Continuous-time Systems

2006 6th World Congress on Intelligent Control and Automation ◽

10.1109/wcica.2006.1714121 ◽

2006 ◽

Cited By ~ 3

Author(s):

Bin Jiang ◽

M. Staroswiecki

Keyword(s):

Continuous Time ◽

Fault Tolerant ◽

Fault Tolerant Control ◽

Actuator Fault ◽

Continuous Time Systems ◽

Time Systems

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Parameter-dependent actuator fault estimation for vehicle active suspension systems based on RBFNN

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407021993014 ◽

2021 ◽

pp. 095440702199301

Author(s):

Wenping Xue ◽

Pan Jin ◽

Kangji Li

Keyword(s):

Fault Tolerant ◽

External Disturbance ◽

Active Suspension ◽

Fault Estimation ◽

Linear Matrix ◽

Actuator Fault ◽

Mass Variation ◽

Fe Method ◽

Comparison Results ◽

Parameter Dependent

The actuator fault estimation (FE) problem is addressed in this study for the quarter-car active suspension system (ASS) with consideration of the sprung mass variation. Firstly, the ASS is modeled as a parameter-dependent system with actuator fault and external disturbance input. Then, a parameter-dependent FE observer is designed by using the radial basis function neural network (RBFNN) to approximate the actuator fault. In addition, the design conditions are turned into a linear matrix inequality (LMI) problem which can be easily solved with the aid of LMI toolbox. Finally, simulation and comparison results are given to show the accuracy and rapidity of the proposed FE method, as well as good adaptability against the sprung mass variation. Moreover, a simple FE-based active fault-tolerant control (AFTC) strategy is provided to further demonstrate the effectiveness and applicability of the proposed FE method.

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