State and fault estimation based on fuzzy observer for a class of Takagi-Sugeno singular models

Singular nonlinear systems have received wide attention in recent years, and can be found in various applications of engineering practice. On the basis of the Takagi-Sugeno (T-S) formalism, which represents a powerful tool allowing the study and the treatment of nonlinear systems, many control and diagnostic problems have been treated in the literature. In this work, we aim to present a new approach making it possible to estimate simultaneously both non-measurable states and unknown faults in the actuators and sensors for a class of continuous-time Takagi-Sugeno singular model (CTSSM). Firstly, the considered class of CTSSM is represented in the case of premise variables which are non-measurable, and is subjected to actuator and sensor faults. Secondly, the suggested observer is synthesized based on the decomposition approach. Next, the observer’s gain matrices are determined using the Lyapunov theory and the constraints are defined as linear matrix inequalities (LMIs). Finally, a numerical simulation on an application example is given to demonstrate the usefulness and the good performance of the proposed dynamic system.

Adaptive Observer Based Fault Tolerant Control for Sensor and Actuator Faults in Wind Turbines

The installed wind energy generation capacity has been increasing dramatically all over the world. However, most wind turbines are installed in hostile environments, where regular operation needs to be ensured by effective fault tolerant control methods. An adaptive observer-based fault tolerant control scheme is proposed in this article to address the sensor and actuator faults that usually occur on the core subsystems of wind turbines. The fast adaptive fault estimation (FAFE) algorithm is adopted in the adaptive observers to accurately and rapidly located the faults. Based on the states and faults estimated by the adaptive observers, the state feedback fault tolerant controllers are designed to stabilize the system and compensate for the faults. The gain matrices of the controllers are calculated by the pole placement method. Simulation results illustrate that the proposed fault tolerant control scheme with the FAFE algorithm stabilizes the faulty system effectively and performs better than the baseline on the benchmark model of wind turbines.

Sliding Mode Fault Tolerant Control for a Quadrotor with Varying Load and Actuator Fault

In this paper, an adaptive sliding mode fault-tolerant control scheme based on prescribed performance control and neural networks is developed for an Unmanned Aerial Vehicle (UAV) quadrotor carrying a load to deal with actuator faults. First, a nonsingular fast terminal sliding mode (NFTSM) control strategy is presented. In virtue of the proposed strategy, fast convergence and high robustness can be guaranteed without stimulating chattering. Secondly, to obtain correct fault magnitudes and compensate the failures actively, a radial basis function neural network-based fault estimation scheme is proposed. By combining the proposed fault estimation strategy and the NFTSM controller, an active fault-tolerant control algorithm is established. Then, the uncertainties caused by load variation are explicitly considered and compensated by the presented adaptive laws. Moreover, by synthesizing the proposed sliding mode control and prescribed performance control (PPC), an output error transformation is defined to deal with state constraints and provide better tracking performance. From the Lyapunov stability analysis, the overall system is proven to be uniformly asymptotically stable. Finally, numerical simulation based on a quadrotor helicopter is carried out to validate the effectiveness and superiority of the proposed algorithm.

Simultaneous actuator and sensor fault estimation for neutral-type systems via intermediate observer

This study addresses the fault estimation (FE) issue for neutral-type systems with sensor faults and actuator faults through the intermediate observer. First, it is well-known that the observer matching condition (OMC) ought to be met for most traditional FE methods, which is actually difficult to satisfy for many systems. In order to overcome this limitation, a suitable variable is designed and the intermediate observer is proposed to estimate the actuator and sensor faults for neutral-type systems simultaneously. Second, based on linear matrix inequalities, sufficient conditions are derived, which guarantee the existence of the intermediate observer. An augmented descriptor system is constructed for the neutral-type systems. By the Lyapunov stability theory, states of error systems are ultimately bounded. Finally, two examples demonstrate the effectiveness and practicability of the designed strategy.

Fault detection in a three-tank hydraulic system using unknown input observer and extended Kalman filter

This paper presents the problem of fault diagnosis in a three-tank hydraulic system. A mathematical model of the system is developed in order to apply two different observing algorithms. Unknown Input Observer (UIO) and Extended Kalman Filter (EKF) have been used to detect and isolate actuator and sensor faults. For Unknown Input Observer (UIO), residuals are calculated from the measured and estimated output according to the eigenvalues of the system after processed by Linear Matrix Inequality (LMI). Extended Kalman filter uses process and measurement noise variances for state estimation. Unknown Input Observer and Extended Kalman Filter's performance in fault estimation and isolation is evaluated under different scenarios. Using Extended Kalman Filter (EKF), faults can be diagnosed effectively in the presence of noise, while Unknown Input Observer (UIO) is working better in the absence of noise, and simulation results illustrate that clearly.