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Fault detection, isolation and reconfiguration of the flight control system is an important problem to obtain healthy flight. This paper aims to propose an integrated approach for aircraft fault-tolerant control.

The integrated structure includes a Kalman filter to obtain without noise, a full order observer for sensor fault detection, a GOS (generalized observer scheme) for sensor fault isolation and a fuzzy controller to reconfigure of the healthy sensor. This combination is simulated using the state space model of a lateral flight control system in case of disturbance and under sensor fault scenario.

Using a dedicated observer scheme, the detection and time of sensor fault are correct, but the sensor fault isolation is evaluated incorrectly while the faulty sensor is isolated correctly using GOS. The simulation results show that the suggested approach works affectively for sensor faults with disturbance.

This paper proposes an integrated approach for aircraft fault-tolerant control. Under this framework, three units are designed, one is Kalman filter for filtering and the other is GOS for sensor fault isolation and another is fuzzy logic for reconfiguration. An integrated approach is sensitive to faults that have disturbances. The simulation results show the proposed integrated approach can be used for any linear system.

The purpose of this paper is to propose development of a formation control algorithm by employing a nonlinear observer for compensating the delay in the sensor signal transmission to the controller arising due to packet dropout in acoustic medium.

A robust control law is developed using the sliding mode approach integrated with a communication consensus algorithm for achieving cooperative motion of acoustic underwater vehicles in a group ensuring the transfer of information among the AUVs. In acoustic medium, inter-vehicle communication is challenging for a group of AUVs deployed in formation because underwater channel encounter a number of constraints such as low data rate, packet delays and dropouts.

It is observed that the sliding mode control-unscented Kalman filter formation control exhibits superior control performance such as mitigating larger initial error of estimation and removing the use of the Jacobian matrices among the three controllers developed. The proposed nonlinear observer estimates the un-measureable states such as position in x, y and z-axes, heading, rudder and sturn angle, needed for generating the formation control. A simulation setup is realized to demonstrate the performance of the proposed observer-based formation controller. Simulations were performed in MATLAB and the obtained results are analysed and compared which envisage that the proposed control algorithm provides efficient formation control under the acoustic communication constraints.

Development of observer for achieving formation control of AUVs in underwater area – common reference velocity and error signals being available to all cooperating AUVs – UKO performs better based on initial error estimation and tracking the same path in shallow water area.

The purpose of this paper is to present novel robust fault tolerant control design architecture to detect and isolate spacecraft attitude control actuators and reconfigure to redundant backups to improve the practicality of actuator fault detection.

The Robust Fault Tolerant Control is designed for spacecraft Autonomous Rendezvous and Docking (AR&D) using Lyapunov direct approach applied to non‐linear model. An extended Kalman observer is used to accurately estimate the state of the attitude control actuators. Actuators on all three axes (roll/pitch/yaw) sequentially fail one after another and the robust fault tolerant controller acts to reconfigure to redundant backups to stabilize the spacecrafts and complete the required maneuver.

In the simulations, the roll, pitch and yaw dynamics of the spacecraft are considered and the attitude control actuators failures are detected and isolated. Furthermore, by switching to redundant backups, the guarantee of overall stability performance is demonstrated.

A real time actuator failure detection and reconfiguration process using robust fault tolerant control is applied for spacecraft AR&D maneuvers. Finding an appropriate Lyapunov function for the non‐linear dynamics is not easy and always challenging. Failure of actuators on all three axes at the same time is not considered. It is a very useful approach to solve self‐assembly problems in space, spacecraft proximity maneuvers as well as co‐operative control of planetary vehicles in presence of actuator failures.

An approach has been proposed to detect, isolate and reconfigure spacecraft actuator failures occurred in the spacecraft attitude control system. A Robust Fault Tolerant Control scheme has been developed for the nonlinear AR&D maneuver for two spacecrafts. Failures that affect the control performance characteristics are considered and overall performance is guaranteed even in presence of control actuator failures. The architecture is demonstrated through model‐based simulation.

The purpose of this paper is to present a variable structure multiple model adaptive estimator (VSMMAE) for liaison navigation system. Liaison navigation is an autonomous navigation method where inter-satellite range measurements are used to estimate the orbits of all participating spacecrafts simultaneously.

To overcome the problem caused by an inaccurate initial state, a navigation algorithm is designed based on the multiple model adaptive estimation technique. The multiple models are constructed by different initial error covariance matrices. To reduce the computational cost, the likely-model set (LMS) algorithm is adopted to eliminate the unlikely models.

It is specified that the performance of the liaison navigation based on the extended Kalman filter (EKF) is sensitive to the initial error. Simulation results show that the VSMMAE outperforms the EKF in the presence of a large initial error.

The presented algorithm is applicable to spacecraft autonomous navigation.

A novel navigation algorithm based on the VSMMAE is developed. It is an effective method for the liaison navigation system.

Vector control has very good transient and steady‐state performance in induction motors. Furthermore, most direct stator flux orientation methods do not need speed information and these methods are not sensitive to parameters other than stator resistance. However, the performance of these control strategies depends on accurate estimation of the stator flux. The voltage model is one of the methods used for estimating the stator flux. In this paper, we discuss the integration methods for the voltage model which have an open integration problem, and those which have magnitude and angle errors in the stator flux. We then describe a new compensator to solve the problems associated with the integrator. The limiting level in the feedback loop of this compensator is estimated by using the intersection points of the two phases of the stator flux. The proposed new compensation method, which is computationally fast, has been both simulated and implemented on an experimental system. Experimental results show excellent performance, especially near zero speed.

This paper presents for the first time the analytical solution of the constrained minimization for the on‐line estimation of the electrical parameters of an induction motor. The method is fully described mathematically and its goodness is verified experimentally on a suitably set up test bench. This methodology permits the almost correct computation of all the so called K‐parameters, which is not always the case in current literature, thus resulting in the correct estimation of the electrical parameters.

The paper sets out to deal with the off‐line identification of induction motor (IM) parameters at standstill. Determination of values of the IM parameters is essential in sensorless drives with regard to accuracy and quality of the control system.

The presented identification method is based on the reconstruction of stator current response to the forced stator voltage step change; thus the cost function is defined in the classical form of the mean squared error between the computed and experimental data. The identification via evolutionary algorithms (EAs) is presented. The considered problem is continuous and thus a continuous version of EA is suggested as more suitable.

This approach has been shown to have several advantages over classical optimisation methods like the ability to cope with ill‐behaved problem domains exhibiting attributes such as: discontinuity, time‐variance, randomness, and, what is particularly important in this application, the ability to cope with the signals disturbed by noises. Owing to this ability the EAs could be implemented directly for the identification of IM parameters not only in simulations but also in the industrial applications for the motor control, though the electrical signals acquired from real motor and used as input data in the identification procedures are to a large extent disturbed by the electrical noises.

Two versions of the suggested approach are compared: the EA with hard selection and with soft selection. Both algorithms were tested in a simulation and experimental set‐up using digital signal processor for control and signal processing of the voltage inverter‐fed IM drive. Satisfactory results were obtained for the identification procedure based on the selected EA.

To improve sensorless control of induction motor using Kalman filtering family, this paper aims to introduce a new metaheuristic optimizer algorithm for online rotor speed and flux estimation.

The main problem with unscented Kalman filter (UKF) observer is its sensibility to the initial values of Q and R. To solve the optimal solution of these matrices, a novel alternative called ant lion optimization (ALO)-UKF is introduced. It is based on the combination of the classical UKF observer and a nature-inspired metaheuristic algorithm, ALO.

Synthesized ALO-UKF has given good results over the famous extended Kalman filter and the classical UKF observer in terms of accuracy and dynamic performance. A comparison between ALO and particle swarm optimization (PSO) was established. Simulations illustrate that ALO recovers rapidly and accurately while PSO has a slower convergence.

Using the proposed approach, tuning the design matrices Q and R in Kalman filtering becomes an easy task with a high degree of accuracy and the constraints of time cost are surmounted. Also, ALO-UKF is an efficient tool to improve estimation performance of states and parameters’ uncertainties of the induction motor. Related optimization technique can be extended to faults monitoring by online identification of their corresponding signatures.

This paper aims to focus on the sensor fault-tolerant control (FTC) for civil aircraft under exterior disturbance.

First, a three-step cubature Kalman filter (TSCKF) is designed to detect and isolate the sensor fault and to reconstruct the sensor signal. Meanwhile, a nonlinear disturbance observer (NDO) is designed for disturbance estimation. The NDO and the TSCKF are combined together and an NDO-TSCKF is proposed to solve the problem of sensor faults and bounded disturbances simultaneously. Furthermore, an FTC scheme is designed based on the nonlinear dynamic inversion (NDI) and the NDO-TSCKF.

The method is verified by a Cessna 172 aircraft model under bias gyro fault and constant angular rate disturbance. The proposed NDO-TSCKF has the ability of signal reconstruction and disturbance estimation. The proposed FTC scheme is also able to solve the sensor fault and disturbance simultaneously.

NDO-TSCKF is the novel algorithm used in sensor signal reconstruction for aircraft. Then, disturbance observer-based FTC can improve the flight control system performances when the system with faults.

The NDO-TSCKF-based FTC scheme can be used to solve the sensor fault and exterior disturbance in flight control. For example, the bias gyro fault with constant angular rate disturbance of a civil aircraft is studied.

Signal reconstruction for critical sensor faults and disturbance observer-based FTC for civil aircraft are useful in modern civil aircraft design and development.

This is the research paper studies on the signal reconstruction and FTC scheme for civil aircraft. The proposed NDO-TSCKF is better than the current reconstruction filter because the failed sensor signal can be reconstructed under disturbances. This control scheme has a better fault-tolerant capability for sensor faults and bounded disturbances than using regular NDI control.

Direct Torque Control (DTC) of induction motor drives is a well-established technique owing to features such as fast dynamic and insensibility to motor parameters. However, conventional DTC scheme, based on comparators and the switching table, suffers from large torque and flux ripples. To improve DTC performance, this study aims to propose and implement a sensorless finite-state predictive torque control using extended Kalman Filter in dSPACE environment.

This paper deals with the design of an extended Kalman filter for estimating the state of an induction motor model and for sensorless control of systems using this type of motor as an actuator. A complex-valued model is adopted that simultaneously allows a simpler observability analysis of the system and a more effective state estimation.

Simulation and experimental results reveal that the drive system, associated with this technique, can effectively reduce flux and torque ripples with better dynamic and steady state performance. Further, the proposed approach maintains a constant switching frequency.

The proposed speed observer have been developed and implemented experimentally under different operating conditions such as parameter variation, no-load/load disturbances and speed variations in different speed operation regions.