Fault Estimation and Fault-Tolerant Control of Markovian Jump System With Mixed Mode-Dependent Time-Varying Delays Via the Adaptive Observer Approach

Author(s):  
Dunke Lu ◽  
Xiaohang Li ◽  
Jin Liu ◽  
Guohui Zeng

In this paper, the problem on simultaneous estimation of the actuator and sensor faults is first addressed for a class of Markovian jump systems with mixed mode-dependent time-varying delays. By using a generalized system technique, the original system is first transformed into a descriptor one; its states consist of original states and sensor fault. Then, a Markovian adaptive observer is designed for the descriptor system to provide simultaneous estimations of the state, actuator fault, and sensor fault. In the light of online acquired information, a state-feedback-based fault-tolerant controller is constructed to stabilize the closed-loop system in the presence of the actuator fault. Using the Lyapunov–Krasovskii functions, sufficient and necessity conditions for the existence of designed observer and controller are derived in terms of linear matrix inequalities, which can be solved readily through efficient mathematical tools. Finally, numerical and practical examples are given to validate the effectiveness of the proposed method.

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Bowen Hong ◽  
Lina Yao ◽  
Zhiwei Gao

In this paper, an integrated scheme including fault diagnosis and fault-tolerant controller design is proposed for the manipulator system with the sensor fault. Any constant fault or time-varying fault can be estimated by the fault diagnosis scheme based on the adaptive observer rapidly and accurately, and the designed parameters can be solved by the linear matrix inequality. Using the fault estimation information, a fault-tolerant controller combining the characteristics of the proportional differentiation control and the sliding model control is designed to trace the expected trajectory via the back-stepping method. Finally, the effectiveness of the above scheme is verified by the simulation results.


2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Abdelmounaim Khallouq ◽  
Asma Karama ◽  
Mohamed Abyad

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.


2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Pan Jin ◽  
Wenping Xue ◽  
Kangji Li

This paper is concerned with the problem of actuator fault estimation (FE) for vehicle active suspension systems. First, the fast FE approach, which combines the output error term with its derivative term in the FE algorithm, is extended to the active suspension system with actuator fault and external disturbance input. Then, considering three typical kinds of actuator faults, i.e., constant gain change fault, drift fault, and stuck fault, genetic algorithm (GA) is employed to optimize the adjustable parameters in the FE algorithm, which are usually determined by trials. Finally, simulation results of FE and fault-tolerant control (FTC) are presented to illustrate the effectiveness and applicability of the proposed FE method.


Author(s):  
Wenping Xue ◽  
Pan Jin ◽  
Kangji Li

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.


Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Zhi Wang ◽  
Yateng Bai ◽  
Jin Xie ◽  
Zhijie Li ◽  
Caoyuan Ma ◽  
...  

In order to overcome disturbances such as the instability of internal parameters or the actuator fault, the time-varying proportional-integral sliding-mode surface is defined for coordinated control of the excitation generator and the steam valve of waste heat power generation units, and a controller based on sliding-mode function is designed which makes the system stable for a limited time and gives it good performance. Based on this, a corresponding fault estimation law is designed for specific faults of systems, and a sliding-mode fault-tolerant controller is constructed based on the fixed-time control theory so that the systems can still operate stably when an actuator fault occurs and have acceptable performance. The simulation results show that the tracking error asymptotically tends to be zero, and the fixed-time sliding-mode fault-tolerant controller can obviously improve the dynamic performance of the system.


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