Fault occurrence in real operating systems usually is inevitable and it may lead to performance degradation or failure and requires to be meddled quickly by making appropriate decisions, otherwise, it could cause major catastrophe. This gives rise to strong demands for enhanced fault tolerant control to compensate the destructive effects and increase system reliability and safety in the presence of faults. In this paper, an approach for estimation and control of simultaneous actuator and sensor faults is presented by using integrated design of a fault estimation and fault tolerant control for time-varying linear systems. In this method, an unknown input observer-based fault estimation approach with both state feedback control and sliding mode control was developed to assure the closed-loop system's robust stability via solving a linear matrix inequality formulation. The presented method has been applied to a linear parameter varying system and the simulation results show the effectiveness of this method for fault estimation and system stability.

An algebraic method based on unknown input observer for fault estimation in linear time invariant system with unknown input is implementable if matching condition is satisfied. Matching condition limits practical application of these methods. In this article, a method is proposed for fault estimation which need not satisfy matching condition. Unlike classical methods, the provided method does not require auxiliary output for fault estimation. In the first step, the unknown input is divided in two parts: the matched and the unmatched unknown inputs. Assuming that there exist a dynamic model for the unmatched part, new augmented system is constructed. The augmented system is revealed as a new system with matched unknown input. Then, the effect of matched unknown input is perfectly removed from observer estimation using the traditional unknown input decoupling strategy. In the next step, the full order observer is designed for the augmented system. A fast adaptive law is employed for the fault estimation. Lyapunov stability condition of state and fault estimation are derived by linear matrix inequality (LMI) criteria. The effectiveness of the proposed method is shown via numerical simulation on a flexible joint example.

In this paper, a new separated design of adaptive observer-based fault estimator (FE) and fault-tolerant control (FTC) is introduced for nonlinear systems along with sensor and actuator faults in the presence of external disturbance and parametric modeling uncertainty by T-S fuzzy modeling, which can easily challenge integrated design strategies. Firstly, a new fuzzy adaptive FE estimates actuator/sensor faults, and the system states simultaneously and also can automatically estimate and compensates bi-directional uncertainties between FE function and control system. Secondly, a fuzzy state feedback FTC controller is designed based on these estimations. Then the proposed separated FE/FTC design presented which is solved by linear matrix inequality via H1 optimization. Finally, a tutorial example of an inverted pendulum on a cart demonstrates the validity and effectiveness of the proposed design.

In this paper, a new active robust fault detection method based on combination of bond graph and Unknown Input Observer (UIO) is proposed. The proposed two-stage method will satisfy some desirable criteria of a fault detection system. In the first stage, an UIO based on derived state space representation form from the bond graph model is used to estimate the states and outputs of the system, which are insensitive to disturbances in the system. Then, the obtained Analytical Redundancy Relations (ARRs) are considered based on the output estimation error of the observer, which are sensitive to faults while are robust against the disturbances. This form of residuals is called Error-based Analytical Redundancy Relations (EARRs), which further becomes robust against parametric uncertainties by defining adaptive thresholds on the parameters’ values of bond graph model. Simulation results for an active suspension system are provided to show the great performance of the proposed method.

This paper proposes an optimal H adaptive observer based on Linear Matrix Inequality (LMI) for fault estimation (FE) with a sliding mode adaptive fault tolerance (FTC) controller (ASM) to propose a fault for the three-degree helicopter model. Has been. In this approach, the system states and the effects of the types of faults (shrinkage, loss of efficiency and locking of the operator) occurring in the operator, uncertainties in system dynamics and operators in the presence of external perturbation and noise are estimated by the adaptive observer. The saturation phenomenon is also considered in this study. Since in practice, for the large control signals, the actuators become saturated, the saturation effect of the actuators is modeled and the saturation effect is estimated by the observer. In the end, simulation results are presented on the dynamics of three degrees of freedom helicopter to demonstrate the effectiveness of the proposed approach, which results in satisfactory and satisfactory performance of the proposed algorithm.

Due to the lack of measurement in distribution systems, state estimation has particular importance. Different methods are presented to improve the accuracy of system state with limited measurements. In this paper a new state estimator in distribution systems are offered. This estimator bases on backward forward load flow estimates system state with adjusting load consumption at each step. Voltage measurements in slack bus, loads and zero injection measurements are inputs of estimator. This estimator is compared with weight least square estimator and its results are shown. The estimator calculates voltage magnitude with less error and also faster than WLS estimator. 85-bus system is presented in this paper.

In this paper, estimation problem is considered as a conditional optimization problem and then is solved using both differential evolution (DE) and particle swarm optimization (PSO).The proposed filter searches stochastically along the state space and obtains the best estimation at any time. The performance of the proposed filter, based on differential evolution (DE) and particle swarm optimization (PSO), is compared with extended Kalman filter (EKF) and with particle filter in different conditions for a non-linear system. Simulation results show that the performance of evolution filters in different conditions is superior to extended Kalman and particle filters.

Degradation due to the long time operation at the gas turbine may cause a fall in the combustor and compressor efficiency and characteristics, which makes a decrease in total efficiency and, an increase in fuel consumption with growth in the production of the pollutants. These phenomena are not distinguishable by conventional diagnostic systems. Although instrumental protection of gas turbine may be profited by the control system but, it depends to components damage result revealation. So, in this research, a special method using the Multiple Model approach, based on multi-operating points is developed to continuously estimation these kinds of faults, to sustain the major defects in an industrial gas turbine. The object is done by defining the main components health indicator variables. First nonlinear thermodynamic static and dynamic models are constructed and linearized. By a combination of those in a dynamic convex set the alternate adaptive model is developed that, could cover the dynamic of the gas turbine. By decoupling the adaptive filter residuals, estimation of the faults and operating point determination are executed. Finally, the efficiency of the proposed approach is demonstrated in a simulation environment.