This article investigates the problem of simultaneous attitude and vibration Control of a flexible spacecraft to perform high precision attitude maneuvers and reduce vibrations caused by the flexible panel excitations in the presence of external disturbances, system uncertainties, and actuator Faults. Adaptive integral sliding mode Control is used in conjunction with an attitude actuator Fault iterative learning observer (based on sliding mode) to develop an active Fault Tolerant algorithm considering rigid-flexible body dynamic interactions. The discontinuous structure of Fault-Tolerant Control led to discontinuous commands in the Control signal, resulting in chattering. This issue was resolved by introducing an adaptive rule for the sliding surface. Furthermore, the utilization of the sign function in the iterative learning observer for estimating actuator Faults has not only enhanced its robustness to external disturbances through a straightforward design, but has also led to a decrease in computing workload. The strain rate feedback Control algorithm has been employed with the use of piezoelectric sensor/actuator patches to minimize residual vibrations caused by rigid-flexible body dynamic interactions and the effect of attitude actuator Faults. Lyapunov's law ensures finite-time overall system stability even with fully coupled rigid-flexible nonlinear dynamics. Numerical simulations demonstrate the performance and advantages of the proposed system compared to other conventional approaches.

In this paper, a continuous globally stable tracking Control algorithm is proposed for spacecraft in the presence of unknown actuator failure. The design method is based on nonlinear dynamic inversion and in contrast to traditional Fault-Tolerant Control methods, the proposed Controller does not require knowledge of the actuator Faults and is implemented without explicit Fault detection and isolation processes. The stability proof is based on a Lyapunov analysis and the properties of the singularity free quaternion representation of spacecraft dynamics. Results of numerical simulations state that the proposed Controller is successful in achieving high attitude performance in the presence of external disturbances and actuator failures.

In this paper, a continuous stable tracking Control algorithm is proposed for spacecraft in the presence of unknown actuator failure, Control input saturation and external disturbances. The design method is based on variable structure Control and has the following properties: 1) fast and accurate response in the presence of bounded disturbances; 2) robust to the partial loss of actuator effectiveness; 3) explicit consideration of Control input saturation. In contrast to traditional Fault-Tolerant Control methods, the proposed Controller does not require knowledge of the actuator Faults and is implemented without explicit Fault detection and isolation processes. In the proposed Controller, a single parameter is adjusted dynamically in such a way that it is possible to prove the ultimate boundedness of both attitude and angular velocity errors. The stability proof is based on a Lyapunov direct method and the properties of the singularity free quaternion representation of spacecraft error dynamics. Results of numerical simulations state that the proposed Controller is successful in achieving high attitude performance in the presence of external disturbances, actuator multiplicative Faults, and Control input saturation.

Any defect in the flight Control system may cause an irreparable problem. Typically, a highly reliable system with human decision-making power is used to prevent or correct such errors in a flying vehicle. A Fault Tolerant Control system is designed to deal with various types of errors that may occur in the system. Fault-Tolerant Control systems are divided into two main parts. The first part is the error detection and isolation phase and the second part is the Control system design phase to overcome the error effects in the system, depending on the type of error and the location of the error, whether the sensor, actuator, or components, the Control system must be able to eliminate error effects. In this paper, a neural-adaptive observer is used in the error detection stage, and in the second stage, a Control system is designed based on the back-stepping algorithm. Nonlinear six-degree-of-freedom simulation results for an F-18 aircraft model indicate its suitable efficiency in the detection and compensation of Fault effects.

This paper designs a robust Control system for wind turbines using a fuzzy sliding mode Controller. Due to the increasing use of wind turbines and the importance of reliability and efficiency of these systems, in this paper the tolerance of wind turbine system using a combination of classical nonlinear and intelligent Control methods against the occurrence of possible Faults has been investigated. The wind turbine system based on the permanent magnet synchronous generator has been studied in this paper. In the design of the Fault Tolerant Controller, a combination of the sliding mode Controller based on advanced exponential acquisition law and fuzzy system is employed and the Control goal is to track the reference input when the system is under Fault conditions. Also, the designed Controller is able to reduce the chattering phenomenon. To evaluate the performance of the proposed Control system, simulated Faults with different characteristics such as amplitude, occurrence time and dynamic change speed have been developed. Results confirm that the designed Controller is robust against to actuator Faults in the wind turbine system. Also, the output voltage is perfectly set to the constant reference value.

In this paper, the artificial intelligence is employed to design a Fault-Tolerant Controller (FTC) for structural vibrations. The FTC is designed to reduce the probability of damage considering sensor Fault. For this purpose, Neural Networks (NNs) are used as Fault detection and accommodation and fuzzy logic is used as a Controller. This Control strategy requires two groups of neural networks. The first group of neural networks finds the Faulty sensor by estimating the structural responses and comparing them with the responses obtained from the sensors. The second group has the task of estimating the response of the Faulty sensor using data obtained from healthy sensors. To evaluate this method, the time history analysis of a 3-story benchmark building equipped with accelerometers and active actuators has been used. This evaluation is based on determining the probability of structural damage and the generation of fragility curves under forty ground motions. To develop fragility curves, the criteria specified in the FIMA 356 (IO, LS and CP) for the moment frame based on the inter-story drift are used. This study show that in the absence of the neural networks, sensor Fault reduces the performance of the fuzzy Controller and it is even possible to increase the structural responses compared to the structure without the Controller. In addition, results demonstrate that the proposed Control strategy can rectify the deterioration of sensor Faults and decrease the probability of failure.

A Fault-Tolerant Control (FTC) methodology has been presented for nonlinear processes being imposed by Control input constraints. The proposed methodology uses a combination of Feedback Linearization and Model Predictive Control (FLMPC) schemes. The resulting constraints in the transformed process will be dependent on the actual evolving states, making their incorporation in the design context a non-trivial task. A feasible direction method has been integrated in the design procedure based on active set technique to resolve the challenging constraint–based FLMPC problem. The formulated FLMPC design method is utilized to develop a FTC scheme by providing a set of backup Control configurations for a CSTR benchmark process. The successful performance of the proposed FTC methodology has been demonstrated via a category of common Fault scenarios by exercising an arbitrary replacement of Control configurations through a supervisor to maintain the CSTR operation at an unstable desired steady-state point.

In this paper, the main focus is on blood glucose level Control and the possible sensor and actuator Faults which can be observed in a given system. To this aim, the eligibility traces algorithm (a Reinforcement Learning method) and its combination with sliding mode Controllers is used to determine the injection dosage. Through this method, the optimal dosage will be determined to be injected to the patient in order to decrease the side effects of the drug. To detect the Fault in the system, residual calculation techniques are utilized. To calculate the residual, it is required to predict states of the normal system at each time step, for which, the Radial Basis Function neural network is used. The proposed method is compared with another reinforcement learning method (Actor-Critic method) with its combination with the sliding mode Controller. Finally, both RL-based methods are compared with a combinatory method, neural network and sliding mode Control. Simulation results have revealed that the eligibility traces algorithm and actor-critic method can Control the blood glucose concentration and the desired value can be reached, in the presence of the Fault. However, in addition to the reduced injected dosage, the eligibility traces algorithm can provide lower variations about the desired value. The reduced injected dosage will result in the mitigated side effects, which will have considerable advantages for diabetic patients.

In this paper, an adaptive Fault Tolerant nonlinear Control is proposed for attitude tracking problem of satellite with three magnetorquers and one reaction wheel in the presence of inertia uncertainties, external disturbances, and actuator Faults. Firstly, sliding surface variable is chosen based on avoiding the singularity of Control signal and guaranteeing the convergence of attitude tracking error to zero in a finite-time. Subsequently, modified non-singular fast terminal sliding mode is designed as Fault Tolerant Control approach. Then, the Control gain of reaching law is adaptively designed to improve the performance of proposed Controller. The adaptive Control gain is designed independent of the upper and lower bounds of the actuator effectiveness factors. Stability proof is performed by Lyapunov function candidate to show that attitude tracking errors and angular velocities are converged to zero. To evaluate the performance of proposed method, simulation results are compared with their non-adaptive version. Outcomes show better performance of the proposed Controller in tracking the desired attitude, a significant reduction in convergence time, and reduction in the chattering of Control torque.

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.