This study is concerned with the state estimation and fault reconstruction problems for a class of discrete systems with integral measurements under partially decoupled disturbances. The considered integral measurements, as functions of the system states over a period of time, reflect the interval time between sample collections and real-time signal processing. Moreover, the process disturbances are allowed to be partially decoupled in the observer design. An augmented state vector is constructed, which consists of the current system state, the delayed system state and the additive fault, and the resultant augmented system is described in a singular form. Then, an unknown input observer is obtained that decouples partial disturbances and attenuates the effect from the remaining undecouplable disturbances. The existence conditions of the desired observer are thoroughly investigated and an algorithm for designing the observer gains is also provided. Finally, a numerical example is presented to show the effectiveness of the proposed method.

State estimation and fault reconstruction with integral measurements under partially decoupled disturbances

This paper concerns the problem of state, fault estimation (FE) and Fault Tolerant Control (FTC) of Takagi-Sugeno (T-S) descriptor systems affected by sensor, actuator and external disturbances simultaneously. An Adaptive Fuzzy Observer is firstly proposed to achieve a simultaneous estimation of descriptor system states, actuator and sensor faults by using the H∞ optimization technique. A FTC is secondly proposed to stabilize the faulty descriptor system. Based on Lyapunov method, stability analysis and design conditions of the resulting closed-loop system are formulated in a set of Linear Matrices Inequalities (LMIs). The adaptive fuzzy observer and the FTC are independently designed, in order to avoid the coupling problem. Accordingly, the observer and controller gains are computed separately by solving a set of LMIs and then used to estimate the unmeasured states, sensor and actuator faults at the same time. Finally, a truck-trailer system application is given to illustrate the validity of the proposed approach.

Adaptive Observer and Fault Tolerant Control for Takagi-Sugeno Descriptor Nonlinear Systems with Sensor and Actuator Faults

This paper studies the fault detection problem for a class of hypersonic vehicle with actuator faults, disturbances and random noises. To handle the unknown disturbances, an unknown input Kalman filter (UIKF) is presented to estimate the unknown system states and disturbances, simultaneously. Considering that the closed-loop structure brings the robustness to the hypersonic vehicle, which could cover some faults, the Total Measurable Fault Information Residual (ToMFIR) is employed as the fault detection residual. Moreover, to deal with the random noises, the hypothesis testing method is utilized to obtain the thresholds under some fault detection performances (false alarm rate and missing alarm rate). The fault detectability condition is also derived. Finally, the simulations verify the effectiveness of the proposed fault detection method.

Fault Detection for a Class of Closed-loop Hypersonic Vehicle System via Hypothesis Test Method

The problem of IMU (Inertial Measurement Units) sensor fault diagnosis of the quadrotor UAV (Unmanned Aerial Vehicle) attitude control system is considered is this paper. Meanwhile, the non-linearity of the quadrotor model and the external disturbances are both considered. To get more accurate fault estimation results UIO (Unknown Input Observer) is selected, because it is performed by the interference decoupling method, can handle disturbances as unknown inputs, and has strong robustness to non-linearity. Besides, in order to eliminate sensor faults effect on the quadrotor UAV system, to ensure that the quadrotor UAV can still maintain normal operation after sensor fault occurs. A feedback compensation fault-tolerant controller is designed after accurate sensor fault estimation is obtained. Finally, MATLAB simulation results proved the effectiveness of the fault diagnosis scheme and the fault compensation method proposed in this paper.

UIO Based Sensor Fault Diagnosis and Compensation for Quadrotor UAV

Signal frequency domain analysis and sensor fault diagnosis based on artificial intelligence

In this study, a novel robust fault diagnosis scheme is developed for a class of nonlinear systems when both fault and disturbance are considered The proposed scheme includes both component and sensor fault with nonlinear system that transferred to nonlinear Takagi-Sugeno (T-S) model It considers a larger category of nonlinear system when fuzzification is used for only nonlinear distribution matrices In fact the proposed method covers nonlinear systems could not transform to linear T-S model This paper studies the problem of robust fault diagnosis based on two fuzzy nonlinear observers, the first one is a fuzzy nonlinear unknown input observer (FNUIO) and the other is a fuzzy nonlinear Luenberger observer (FNLO) This approach decouples the faulty subsystem from the rest of the system through a series of transformations Then, the objective is to design FNUIO to guarantee the asymptotic stability of the error dynamic using the Lyapunov method; meanwhile, FNLO is designed for faulty subsystem to generate fuzzy residual signal based on a quadratic Lyapunov function and some matrices inequality convexification techniques FNUIO affects only the fault free subsystem and completely removes any unknown inputs such as disturbances when residual signal is generated by FNLO is affected by component or sensor fault This novelty and using nonlinear system in T-S model make the proposed method extremely effective from last decade literature Sufficient conditions are established in order to guarantee the convergence of the state estimation error Thus, a residual generator is determined on the basis of LMI conditions such that the estimation error is completely sensitive to fault vector and insensitive to the unknown inputs Finally, an numerical example is given to show the highly effectiveness of the proposed fault diagnosis scheme

Robust fault diagnosis scheme in a class of nonlinear system based on UIO and fuzzy residual

In this study, a novel fuzzy unknown input observer for robust fault estimation scheme is developed when both faults and unknown input are considered. The proposed scheme includes component fault with nonlinear distribution matrix in state equation, unknown input signal in state and output equations. After that, Takagi-Sugeno (T-S) model is used to create multiple models. While T-S model is used for only the nonlinear distribution matrix of the fault signal, a larger category of nonlinear system will be included. Two set of observers are considered, the first one is extended fuzzy unknown input observer (EFUIO) and the other one is fuzzy sliding mode observer (FSMO). The approach decoupled the faulty subsystem from the rest of the system through a series of linear transformations. Then, the objective is to design EFUIO to guarantee the asymptotic stability of the error dynamic using the Lyapunov method. Unknown input is removed; meanwhile, FSMO is designed for faulty subsystem to guarantee estimation of fault. Sufficient conditions are established in order to guarantee the convergence of the state estimation error and the results are formulated in the form of linear matrix inequalities (LMIs). Finally, a simulation study on an electromagnetic suspension system (EMS) is presented to demonstrate the performance of the results compared with a pure SMO.

S. Hamideh Sedigh Ziyabari

Papers

Robust fault diagnosis scheme in a class of nonlinear system based on UIO and fuzzy residual

Robust fuzzy fault estimation based on decoupled transform and unknown input sliding mode observer