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In this article, a fault-tolerant tracking control technique is developed for the ascent of hypersonic vehicles (HSV) experiencing wing damage and time-varying state constraints. The difficulty of control problem lies in handling state constraints associated with both state and time, while determining the unknown control directions of the multi-input multi-output (MIMO) nonlinear system. The presence of constraints in both state and time complicates the design of the control algorithm. This paper proposes the construction of a novel state transition function to establish a new unconstrained system, thereby eliminating the conservative feasibility conditions of Barrier Lyapunov Function (BLF) and integral BLF methods when dealing with time-varying constraints. The fusion of time-varying scaling functions and Nussbaum-type functions not only resolves the issue of unknown control directions in the MIMO nonlinear system but also enhances the transient and steady-state performance of the closed-loop system. Neural Networks are employed to approximate the unknown nonlinear functions. Using Lyapunov stability theory, it is proven that state constraints are not violated, and all signals in the closed-loop system are bounded. Simulation results demonstrate the merits of the designed fault-tolerant control (FTC) algorithm. • A unified fault-tolerant control scheme for the ascent hypersonic vehicle is designed. • Wing damage and function constraints on states are considered. • Nonlinear mapping eliminates the feasibility conditions in barrier Lyapunov functions. • The unknown control direction issue in multivariable nonlinear systems is resolved. [ABSTRACT FROM AUTHOR]

The problem of retargeting rigid body attitude with attitude constraints has been extensively investigated, whereas the challenge persists in maneuvering rigid-body attitude with multiple concurrent attitude constraints and finite sequential actuator faults. Finite sequential actuator faults, which can reduce controllability or destabilize the system, necessitate the implementation of fault-tolerant control strategies. Therefore, this paper aims to address the issue of redirecting spacecraft attitude subject to multiple attitude constraints and finite sequential actuator faults comprehensively. Firstly, we represent spacecraft attitude using unit quaternions and employ an artificial potential function to effectively handle multi-attitude constraints. The negative gradient direction guides the rigid-body spacecraft towards seamless convergence with the desired attitude. Our proposed unwinding strategy effectively avoids unwinding phenomena during large-angle spacecraft reorientation as well. Additionally, we design an adaptive compensation strategy for finite sequential actuator faults that enables real-time calculation of the fault compensation matrix under such actuator faults. Importantly, we propose an improved adaptive fault-tolerant back-stepping controller integrated with a potential function that effectively addresses both attitude constraints and finite sequential actuator faults simultaneously. Subsequently, stability analysis is conducted on the proposed controller to ensure its stability in practical applications rigorously. Finally, numerical simulations are performed meticulously to demonstrate the effectiveness and robustness of our proposed controller. [ABSTRACT FROM AUTHOR]

This paper addresses the fault-tolerant attitude tracking control problem for spacecraft with limited communication capability and input saturation. The design of a hysteresis quantizer aims to alleviate communication burden between the controller module and the actuator module. Then, the attitude tracking problem for spacecraft with input quantization, input saturation, actuator faults and external disturbances is transformed into an attitude control problem with uncertain input coefficients and bounded disturbances. Thereafter a dynamic loop gain function-based approach and a hyperbolic tangent function term with a time-varying boundary layer are introduced to address the uncertainties of input coefficients and the bounded disturbances, respectively. To facilitate the boundness analysis of the signals in the closed-loop system, a pertinent lemma about the dynamic loop gain function is proved. Finally, numerical simulations are employed to validate the effectiveness of the proposed scheme. • The spacecraft attitude tracking problem with disturbances and uncertainties. • The quantized control law alleviates communication burdens of spacecraft. • A dynamic loop gain function method is used to address the impact of uncertain. • A hyperbolic tangent term is devised to counteract bounded disturbances. [ABSTRACT FROM AUTHOR]

Due to the widespread application and significant investment required for a single crane, there is an increased emphasis on crane safety and service life. Fault-tolerant control as an effective solution to unexpected faults has been widely studied recently. However, most fault-tolerant control methods require redundant actuators or a complex design process, which is unsuitable for the tower crane. Following these problems, a fault-tolerant controller based on an adaptive backstepping technique is proposed. Firstly, the system states are reconstructed and written as a cascade system. Secondly, a fixed-time convergence optimized backstepping controller is proposed to achieve smooth control of the tower crane without generating sudden or abrupt values. Then, an adaptive approach has been proposed to update fault parameters for the crane system in case of a sudden fault occurrence. Finally, after conducting comparison tests, it has been determined that the proposed controller not only performs exceptionally well in terms of position accuracy and swing elimination, but also maintains a satisfactory control performance when faced with sudden faults. • The jib and trolley positioning and load swing suppression problems for tower cranes are investigated. • The adaptive backstepping fault-tolerant controller is designed. • The control performance is demonstrated by comparative experiments. [ABSTRACT FROM AUTHOR]

It is the first report about fault-tolerant-based prescribed performance control of switched nonlinear systems under multiple faults. The concerned faults include not only external faults but also actuator faults. In the process of backstepping control design, prescribed performance control is fully considered, and the combination of unknown nonlinear functions is estimated by multi-dimensional Taylor network. Finally, the developed adaptive fault-tolerant control strategy guarantees the boundedness of all controlled signals while prescribed tracking performance is satisfied. In an effort to further manifest the validity of the fault-tolerant controller, a numerical simulation and a practical simulation are introduced. • This paper is dedicated to balancing prescribed tracking performance and multiple faults. • It is the first time that actuator fault, external fault are discussed in the complicated switching framework. • The adaptive fault-tolerant MTN-based controller is developed to cope with performance constraints. [ABSTRACT FROM AUTHOR]

The research investigates the fixed-time command-filtered composite adaptive neural fault-tolerant (FCCANF) control issue of strict-feedback nonlinear systems (SFNSs). There exist unknown functions and bounded disturbances in the considered systems. Radial basis function neural networks (RBFNNs) will be used in the estimate of the unknown functions. By the serial–parallel estimation models (SPEMs), the forecast biases and the track biases can change the weights of RBFNNs and the approximate characteristics of RBFNNs will be improved. Then, utilizing the novel fixed-time command filter and adaptive disturbance observers, the issue of complex explosion will be effectively solved and the external disturbance is effectively compensated. Subsequently, by utilizing the adaptive control technique, a novel FCCANF controller is developed. Additionally, we have that the system internal variables are bounded and the output variable inclines to a little interval around zero in fixed time which is not determined by the system initial variables. Eventually, numerical and practical examples are shown to prove the availability of the obtained control technique. • For the paper [23], a second-order fixed-time command filter with the symbolic function is introduced to estimate the derivatives of the virtual controllers. However, in this manuscript, a first-order fixed-time command filter with the smooth hyperbolic tangent function is designed to estimate the derivatives of the virtual controllers. Then, the compensation of filtering errors is not considered in the paper [23]. However, in this manuscript, the compensation of filtering errors is considered. • To improve the RBFNN approximation, the novel fixed-time convergence SPEMs are created and it is the first time to use the matrix element technique to solve the fixed-time patchwork problems for the adaptive laws. Then, to estimate the lumped disturbance, the fixed-time convergence adaptive neural disturbance observers are utilized firstly. • Compared with the papers [22] and [24], based on [25], the hyperbolic tangent functions are introduced to solve the singular problem. [ABSTRACT FROM AUTHOR]

This paper investigates the fault-tolerant prescribed performance control problem for a class of multiple-input single-output unknown nonlinear systems subject to process faults and actuator failures. In contrast to the related works, we consider a general class of nonlinear systems with both multiplicative nonlinearities and additive nonlinearities corrupted by the process faults; only the boundedness of the process faults and the continuity of the nonlinear functions are required, without the explicit or fixed structures of the fault functions. To conquer this problem, a less-demanding and low-complexity fault-tolerant prescribed performance control approach is proposed. The controller is independent of the specific information of faults or the system model and does not invoke fault diagnosis or neural/fuzzy approximation to acquire such knowledge. It achieves the reference tracking with the predefined rate and accuracy. A comparative simulation on a single-link robot is conducted to illustrate the effectiveness and superiority of the proposed approach. • Both process faults and actuator failures are considered. • The virtual control coefficients are time-varying nonlinear functions. • We consider a general class of systems with multiplicative and additive nonlinearities. • Both multiplicative and additive nonlinearities are impacted by process faults. • The reference tracking with predefined rate and accuracy is achieved. [ABSTRACT FROM AUTHOR]

The paper proposes a data-driven fault-tolerant control (FTC) strategy to construct and accommodate the bias on ambient temperature measurements in supermarket refrigeration systems. The bias, which is caused by direct or indirect exposure of the sensor to the sun, can have a significant impact on the refrigeration system's energy consumption. Based on analysis of the real data a comprehensive model of the bias is developed and then used to generate realistic scenarios for testing the proposed FTC method. The FTC method uses a feed forward Neural Network (NN) as a black box model. The model is trained by active injection of perturbation signals during the night operations. During the Monte-Carlo tests, the strategy was implemented in a Plug & Play manner, demonstrating that substantial energy savings can be achieved during summer periods. • Temperature bias modeling and validation using real data. • Providing a data-driven algorithm to estimate the bias. • Proposing a structure to compensate the bias. • Monte-Carlo simulations to test the proposed structure. [ABSTRACT FROM AUTHOR]

This article scrutinizes the stabilization and fault reconstruction issues for interval type-2 fuzzy-based cyber–physical systems with actuator faults, deception attacks and external disturbances. The primary objective of this research is to formulate the learning observer system with the interval type-2 fuzzy technique that reconstructs the actuator faults as well as the immeasurable states of the addressed fuzzy based model. Further, the information of reconstructed actuator faults is incorporated in the developed controller with the imperfect premise variables for ensuring the stabilization of the system under consideration. At the same time, the H ∞ technique is employed to reduce the impact of external disturbances in the considered model. In addition to that, the deception attacks are represented as a stochastic variable that satisfies the Bernoulli distributions. On the ground of this, a set of sufficient criteria is deduced in the context of linear matrix inequalities to affirm the stability of the addressed systems. Furthermore, the requisite gain matrices are computed by resolving the obtained linear matrix inequality based stability criteria. At last, two simulation examples, including the mass–spring–damper system are exhibited to demonstrate the usefulness of analytical findings of the developed strategy. [ABSTRACT FROM AUTHOR]

Aiming at the perturbation of motion capability and the deviation of end effector from its desired trajectory caused by joint effectiveness partial loss failure, a fault-tolerant control strategy considering motion capability optimization is proposed to ensure that the faulty space manipulator can carry on-orbit operation tasks. Firstly, the joint effectiveness partial loss failure model is established, and the kinematic and dynamic model of the faulty manipulator is established based on the failure model. The kinematic and dynamic coupling relationship of the faulty manipulator is also analyzed. Then based on sliding mode control, the fault-tolerant control strategy that ensures the end effector trajectory tracking and base orientation deflection attenuation is designed. Meanwhile, the motion capability optimization model is designed based on the redundancy characteristic of the manipulator and combined with the fault-tolerant control system. This combined system can realize end effector trajectory tracking, base orientation deflection attenuation, and motion capability optimization at the same time. Finally, simulation experiment is carried out to verify the proposed fault-tolerant control strategy for the faulty manipulator. The result shows that the proposed fault-tolerant control strategy can realize end effector trajectory tracking, base orientation deflection attenuation, and motion capability optimization with high control accuracy, high convergence rate, and significant motion capability optimization effect. For both joint velocity partial loss and joint torque partial loss failure, the duration of convergence is less than 0.5 s, the steady-state error is less than 1 × 10 - 4 m for end effector position and less than 1 × 10 - 5 degree for base orientation deflection, and the motion capability indexes have been raised by over 40%. [ABSTRACT FROM AUTHOR]

This paper addresses the control problems of distributed attitude coordination for multiple flexible spacecraft based on the feedback of inertial vector measurements. It is assumed that the angular velocity information is not available, and the modal variable cannot be measured. The orientation error model with vector measurements is introduced. And then the distributed attitude synchronization control scheme is presented without actuator faults or disturbances. It is guaranteed that the desired equilibrium is locally asymptotically stable and the undesired equilibrium is unstable. Meanwhile, the domain of attraction is given by inequality constraints. In the case that there exist actuator faults and external disturbances, a robust fault-tolerant control strategy is developed to realize attitude synchronization. The hyperbolic tangent function in place of sign function is adopted to eliminate external disturbances. Meanwhile, the adaptive laws are proposed to estimate actuator faults. The salient feature of these control approaches is that no modal variable observers are utilized and the control schemes are robust to the inertia uncertainty of spacecraft. Finally, numerical simulations are implemented to manifest the validity of the presented control schemes. [ABSTRACT FROM AUTHOR]

• An adaptive fault-tolerant control framework for spacecraft formation flying is proposed. • A special function is introduced to guarantee the prescribed performance. • A nonsingular fast terminal sliding mode control scheme is adopted to account for external disturbances and uncertainties. This paper investigates an adaptive fault-tolerant control problem for spacecraft formation flying (SFF) with actuator faults, external disturbances, and unknown inertial uncertainties. First, fault factors are introduced to describe the SFF orbit model with consideration of actuator bias faults and loss of actuator effectiveness. Then, an adaptive fault-tolerant controller is proposed by incorporating prescribed performance function, improved terminal sliding mode control and the proposed adaptive law. Finally, rigorous theoretical analysis ensures the uniformly ultimately bounded stability of the system through Lyapunov theory and simulation verifies the proposed algorithm. [ABSTRACT FROM AUTHOR]

To enhance the robustness of ship autopilot (SA) system with nonlinear dynamics, unmeasured states, and unknown steering machine fault, an observer-based H ∞ fuzzy fault-tolerant switching control for ship course tracking is proposed. Firstly, a global Takagi–Sugeno (T-S) fuzzy nonlinear ship autopilot (NSA) is developed with full consideration of ship steering characteristics. And the actual navigation data collected from a real ship are used to verify the reasonableness and feasibility of NSA model. Then, virtual fuzzy observers (VFOs) for both fault-free and faulty systems are proposed to estimate the unmeasured states and unknown fault simultaneously, and compensate for the faulty system by using the fault estimates. Accordingly, the VFO-based H ∞ robust controller (VFO-HRC) and fault-tolerant controller (VFO-HFTC) are designed. Subsequently, a smoothed Z-score-based fault detection and alarm (FDA) is developed to provide switching signals for which the controller and its corresponding observer should be invoked. Finally, simulation results on the "Yulong" ship demonstrate the effectiveness of the developed control method. • A novel observer-based H ∞ fuzzy fault-tolerant switching control for ship course tracking is proposed. • The novel virtual fuzzy observers (VFOs) are introduced to estimate the unmeasured states and unknown bias fault simultaneously. • A novel fault detection and alarm (FDA) is designed to provide the switching signal for the system to invoke the appropriate controller and corresponding observer. [ABSTRACT FROM AUTHOR]

This paper investigates the attitude fault-tolerant stabilization problem for a spacecraft subjected to its actuator effectiveness loss, inertia uncertainties and space disturbances. A novel Iterative Learning Sliding Mode Observer (ILSMO) is proposed to reconstruct the actuator effectiveness factors robustly and accurately by combining the P-type iterative learning algorithm with the sliding mode approach. Based on the reconstructed fault signals, an Iterative Learning Sliding Mode Controller (ILSMC) is designed to guarantee the closed-loop spacecraft attitude fault-tolerant stabilization by compensating for its lumped disturbance. The ILSMO and ILSMC stabilities are guaranteed using the Lyapunov direct approach, respectively. Finally, the numerical simulation results show that the proposed ILSMO-ILSMC-based spacecraft attitude fault-tolerant stabilization method is effective and superior. [ABSTRACT FROM AUTHOR]

In this paper, the issue of event-triggered optimal fault-tolerant control is investigated for input-constrained nonlinear systems with mismatched disturbances. To eliminate the effect of abrupt faults and ensure the optimal performance of general nonlinear dynamics, an adaptive dynamic programming (ADP) algorithm is employed to develop a sliding mode fault-tolerant control strategy. When the system trajectories converge to the sliding-mode surface, the equivalent sliding mode dynamics is transformed into a reformulated auxiliary system with a modified cost function. Then, a single critic neural network (NN) is adopted to solve the modified Hamilton–Jacobi–Bellman (HJB) equation. In order to overcome the difficulty that arises from the persistence of excitation (PE) condition, the experience replay technique is utilized to update the critic weights. In this study, a novel control method is proposed, which can effectively eliminate the effects of abrupt faults while achieving optimal control with the minimum cost under a single network architecture. Furthermore, the closed-loop nonlinear system is proved to be uniformly ultimate boundedness based on Lyapunov stability theory. Finally, three examples are presented to verify the validity of the control strategy. [ABSTRACT FROM AUTHOR]

• A fault-tolerant model-free predictive approach is developed to control spacecraft coupled 6-DOF relative motion. In this paper, the previous work of the authors on Subspace Predictive Control (SPC) in the context of spacecraft roto-translational relative motion is pursued, and the introduced novel SPC-based approach to fault-tolerant control (FTC) of nonlinear time-variant systems is further investigated. Firstly, the effectiveness of the proposed SPC-based framework for adaptive control allocation is exploited. Subsequently, separating nonlinear control and control allocation blocks yields a new layered SPC-based control scheme. Furthermore, designating a dedicated controller for each of the coupled motions leads to a novel distributed SPC-based architecture. Accordingly, three complete model-free fault-tolerant controllers for coupled nonlinear time-variant plants are developed, which solely need the knowledge of the occurrence time of faults as prerequisite. An internal fault diagnosis capability is also introduced, which makes the framework completely self-sufficient. Finally, the proposed framework is verified by challenging simulated scenarios. [ABSTRACT FROM AUTHOR]

This study investigates the fault-tolerant control problem associated with pose tracking for coupled spacecraft in the presence of both actuator faults and actuator saturation constraint. By using the Lie group SE(3), a six-degrees of freedom (6-DOF) coupled kinematics and dynamics model is established to derive the relative motion tracking errors in the exponential coordinates. Then, all system states are reconstructed to develop a dynamic surface fault-tolerant controller. Considering the lumped disturbance consisting of internal uncertainties, external unknown disturbances and actuator faults, a fuzzy adaptive method is used to estimate and compensate for the lumped disturbance. Next, the controller is nested with an auxiliary first-order filter to compensate for the saturation constraints. A Lyapunov analysis is used to prove the stability and finite time convergence of the proposed controller. Finally, The tracking performance is verified through a numerical example. [ABSTRACT FROM AUTHOR]

Based upon hybrid-triggered mechanism, this article inspects fault-tolerant issue based (Q , S , R) dissipative control for neural networks system along with malcious attacks and external disturbances. To be precious, conventional Luenberger observer is employed to estimate the system state and malicious attack signal, whereas the presence of smoothed signal of malicious attack in the system state will not allow the augmented state to be precisely estimate. Moreover, the hybrid-triggered mechanism which incorporates both time- and event-triggered scheme is initiated to mitigate the redundancy of network transmission and secures the network resources. Preciously, the stochastic switching within the mechanism satisfies the Bernoulli distribution. The main intention of this study is to develop a hybrid-triggered scheme and a fault-tolerant controller for ensuring the mean-square asymptotic stability for the desired neural networks with (Q , S , R) dissipative performance index. Assisted by Lyapunov stability theory and some inequality techniques, an adequate criterion has been attained in the configuration of linear matrix inequalities (LMIs) to guarantee the asymptotic stability of the neural networks system. Further, the anticipated gain matrices are attained with the strength of obtained LMIs. In the final analysis, the applicability and efficacity of the proposed control model are reflected through two numerical examples. [ABSTRACT FROM AUTHOR]

The problem of controlling fractional-order nonlinear systems remains interesting and challenging due to the powerful and hereditary memory features of such systems. In this paper, we investigate the adaptive tracking control problem for a class of fractional-order nonlinear systems with or without state constraints. The tan-type barrier Lyapunov function (BLF) is adopted for the first time in fractional Lyapunov direct method. Such technique allows for the accommodation of the situations with and without state constraints in a unified framework. Moreover, neural network (NN) is utilized to reconstruct the sophisticated nonlinear functions arising from the fractional-order differentiation of the virtual controller, resulting in a neuroadaptive fault-tolerant control scheme. It is shown that, with the proposed unified control, all the closed-loop signals are semiglobally ultimately uniformly bounded whether state constraints are imposed or not. Both theoretical analysis and numerical simulation confirm the effectiveness of the developed approach. [ABSTRACT FROM AUTHOR]

In this article, the design and experimental evaluation of a fault-tolerant controller are introduced for a double-rod electro-hydraulic actuator subjected to actuator faults and disturbances. The internal leakage fault is captured as a bias fault, whilst the faults in servo-valve and supply failure are considered as a partial loss of effectiveness (LOE) fault. The design obstacles caused by the disturbances and bias fault are suppressed by nonlinear disturbance observers (NDO) while an asymmetric barrier Lyapunov function is used to ensure the non-violated boundary of the output position. To tackle the LOE fault, the development of an enhanced adaptive compensation technique for actuator fault-tolerant control (FTC) is then constructed. Moreover, to mitigate the "explosion of complexity" in the traditional backstepping design, the command-filtered control is utilized to elaborate the FTC scheme. It is shown by theoretical analysis that system stability is ensured under faulty conditions. Finally, simulation/experiment results and comparison studies are performed to further verify the effectiveness of the proposed approach. • The lumped disturbances, bias fault and a partial LOE faults is firstly considered for EHA. • The NDOs and an enhanced adaptive law is developed to tackle bias fault and LOE fault. • The proposed FTC method based on CFC approach and BLF is performed. • The stability of the closed-loop system is ensured based on the Lyapunov theory. • Comparative experimental results confirm the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

This work deals with the problem of passive fault-tolerant control (FTC) for discrete-time networked control systems (NCSs). Network imperfections such as random time delay and packet dropout are modeled as a Markov chain that results in a Markovian jump linear system (MJLS). Some of the elements in the transition probability matrix (TPM) are supposed to be unknown so as to address network complexities. In addition, a comprehensive and practical fault model that considers the stochastic nature of networks is employed. By utilizing this fault model, the closed-loop NCS model is obtained by means of state augmentation technique. Then, a constrained model predictive control (MPC) is proposed to develop a fault-tolerant control strategy in which all these issues are considered as well as input constraint. Sufficient conditions to design the proposed reliable controller are derived in terms of linear matrix inequalities (LMIs). Finally, two examples are utilized to demonstrate the validity of the proposed FTC. The simulation results show that the proposed strategy works well, and results in more effective responses compared to state-of-the-arts studies. [ABSTRACT FROM AUTHOR]

This article investigates the challenging problem for the fixed-time trajectory tracking control of free-flying space manipulator under output constraints and actuator faults. A novel fixed-time neural adaptive fault-tolerant control approach is proposed by incorporating the barrier Lyapunov function (BLF) and neural network (NN) into the backstepping control design. The BLF is introduced to preserve the position tracking errors always within the predefined output constraints. Moreover, the NN approximation is adopted to compensate for the lumped uncertain term in the feedforward loop. Lyapunov analysis shows the practical fixed-time stability of the resulting closed-loop system. The proposed controller can guarantee the position and velocity tracking errors converge to the small neighborhoods about the origin in fixed time even in the presence of output constraints and actuator faults and thus ensuring safety. Lastly, the effectiveness and excellent tracking performance of the proposed control approach are illustrated through simulations and comparisons. • The fixed-time trajectory tracking control of free-flying space manipulator is addressed. • A novel fixed-time neural adaptive fault-tolerant control approach is proposed. • The BLF is introduced to preserve the position tracking errors within the output constraints. • The NN approximation is adopted to compensate for the lumped uncertain term. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive modified sliding mode control approach is developed for attitude tracking of a nano-satellite with three magnetorquers and one reaction wheel. A sliding variable is chosen based on finite-time convergence of the nano-satellite attitude tracking error and avoiding the singularity of the control signal. The control gain of the proposed method is developed adaptively to reduce the tracking error and improve the closed-loop control performance. The sliding variable and adaptive parameter are also employed in the reaching phase of the control law to decrease the chattering phenomenon. In addition, the finite-time convergence of attitude variables in the presence of actuator faults, inertia uncertainty, and external disturbances is proved using the extended Lyapunov theorem. The simulations are conducted to evaluate the performance of the proposed method according to different evaluation criteria. Monte Carlo simulations are also used to survey the reliability of the system in the presence of the mentioned condition. [ABSTRACT FROM AUTHOR]

Reliable and real-time active diagnosis of system faults with uncertainties is strongly dependent on the input design. This paper establishes a data-driven framework for integrated design of active fault diagnosis and control while ensuring the tracking performance. To be specific, the input design is formulated as a constrained optimization problem that can be solved with the aid of constrained reinforcement learning algorithms. Moreover, based on the maximum mean discrepancy metric, a novel active fault isolation scheme is proposed to implement model discrimination using system outputs. At the end, the effectiveness of the proposed approach is evaluated in two case studies in the presence of probabilistic disturbances and uncertainties. • A data-driven framework for active fault diagnosis with tracking performance guarantees. • Constrained reinforcement learning is applied in the integrated design of active inputs. • A fault isolation strategy to implement model discrimination based on system outputs. [ABSTRACT FROM AUTHOR]

This brief disposes the finite-time anti-unwinding trajectory tracking control problem of the autonomous underwater vehicle (AUV) encountering model uncertainties, ocean disturbances and actuator failures. Primarily, the kinematic model of translational and rotational motions is depicted by unit quaternion in lieu of classical Euler angle such that the AUV's dynamics could be globally and uniquely formulated. Subsequently, two finite-time control strategies are presented here to leave the state variables of AUV can converge to an adjustable region. Additionally, the adaptive laws could be applicable to the existence of actuator faults by utilizing a passive fault-tolerant technology. By integrating the initial value of the scalar quaternion into the sliding mode surface, the proposed controllers are characterized with anti-unwinding property. Then, with the application of hyperbolic tangent function, finite-time stability will be achieved for tracking errors without singularity. Stability of the closed-loop system is verified via the Lyapunov theorem. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed controllers. • The unit quaternion is applied for attitude representation of AUVs. • A novel sliding mode surface is proposed for the achievement of finite-time convergence, anti-unwinding and singularity avoidance. • Actuator faults could be handled by a constructive fault-tolerant control scheme. [ABSTRACT FROM AUTHOR]

This paper makes an investigation on the fault-tolerant control (FTC) problem for a hypersonic reentry vehicle (HSV) in the coexistence of unknown movement of center-of-mass, system input constraint and failure of actuator. Firstly, the dynamics of HSV's attitude system with the unknown factors mentioned above are developed to illustrate the particularity of the researched topic. The influences of unexpected centroid shift on an FTC design can be summarized into the following three parts: unknown system uncertainties, eccentric torque as well as changing system moment of inertia matrix, which are coupled and unknown. Secondly, due to the difficulty in decoupling and estimating these influences (embodied in the output states of the system) one by one, it is the attitude system states observer that is proposed to estimate those detrimental unknown effects. The designed observer is consisted of an adaptive fault observer and an adaptive sliding-mode observer, supporting an innovative adaptive FTC scheme free from the variation of inverse matrix that might be singular due to an unexpected centroid shift. This fault-tolerant controller established in the estimated system states is derived by utilizing the above mentioned observer and adaptive backstepping control in conjunction with adaptive auxiliary compensation systems to handle the system input saturation. Moreover, the convergence of attitude tracking error and the boundedness of all closed-loop signals are achieved in the light of Lyapunov stability theory and boundedness analysis. Ultimately, simulation results are delivered to demonstrate the effectiveness of the proposed FTC scheme. [ABSTRACT FROM AUTHOR]

• Guarantees fixed-time stability; eliminates chattering issues of SMC. • Adaptive algorithm for simultaneous fault accommodation and disturbance suppression. • Estimates upper bound of lumped uncertainties; no need for known uncertainty bounds. • Uses EKF observer for dynamic fault detection with adaptive threshold technique. This note scrutinizes an adaptive fault-tolerant control (FTC) approach tailored for unmanned aerial vehicles (UAVs), addressing the critical need for both fault accommodation and disturbance suppression. Departing from traditional reliance on robust discontinuous control strategies prone to chattering and demanding precise uncertainty bounds, our FTC method ensures fixed-time stability, guaranteeing the convergence of attitude tracking errors to zero. Central to our approach is an adaptive algorithm adept at concurrently estimating unknown actuator faults and upper bounds of lumped uncertainties. Moreover, our adaptive schemes accurately estimate the upper bound of the lumped uncertainty term, encompassing model uncertainties, external disturbances, and unmodeled dynamics, thereby eliminating the need for assuming known bounds on uncertainties. Stability analysis under the developed control law is thoroughly performed using the Lyapunov stability theory. Notably, our strategy employs an extended Kalman filter (EKF) observer for state estimation and fault detection, facilitating fault detection through an adaptive threshold technique dynamically adjusted based on real-time mean and variance of the residual signal. Through comprehensive simulation and experimental validations, our proposed methodology demonstrates significant advancements in ensuring safety and reliability in UAVs. [ABSTRACT FROM AUTHOR]

Sensor fault and output quantization are common issues acting on vehicle platoon, and they may lead to performance deterioration, instability and even insecurity of the platoon. Therefore, this paper investigates the fault-tolerant control (FTC) problem of vehicle platoons with regard to the above two issues. Considering the probabilistic sensor fault and quantized measurement signals, an unknown input observer (UIO) based fault detection algorithm with adaptive threshold is developed for sensor health status monitoring. Then, an augmented vehicle platoon model is constructed by introducing a low-pass output filter, and a robust UIO is established for state reconstruction. Based on the above results, a fault-tolerant control scheme is exploited by employing the back-stepping control method and adaptive radial basis function neural network (RBF NN) approximation technique, which is proved to be capable of achieving the time-domain string stability (TSS) of vehicle platoons in the presence of sensor fault and output quantization. Simulation results demonstrate the effectiveness of the proposed algorithms. • Third-order nonlinear dynamics is adopted for vehicle platoon. • Sensor fault detection is achieved by unknown input observer. • State reconstruction under sensor fault is achieved by unknown input observer. • Mismatched output quantization is considered. [ABSTRACT FROM AUTHOR]

This paper proposes a fault-tolerant control scheme for tracking and controlling hypersonic vehicles with unknown dynamics, actuator failures, and unmeasurable states. The approach involves using a radial-based neural network to approximate the unknown dynamics and reconstruct the entire system model. Additionally, a neural network state observer is proposed to estimate the unmeasurable state of the system. To address the impact of actuator faults, a nonlinear observer is designed to estimate and compensate for the approximation error of the neural network system and fault values. Furthermore, a prescribed performance function is introduced to ensure both transient and steady-state performance of the system. The bounded stability of the closed-loop system is demonstrated through Lyapunov stability analysis. • Unmeasurable states, actuator faults, and state constraints in hypersonic flight vehicle control are investigated. • The neural network state observer realizes the estimation of the unmeasurable state of the system under fault conditions. • The nonlinear observer can estimate perturbations, and the prescribed performance function guarantees transient and steady-state performance. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative adaptive event-triggered fault-tolerant attitude control framework designed for a flying-wing unmanned aerial vehicle (UAV) operating under constraints such as limited embedded resources, unknown actuator failures, system uncertainties, and external disturbances. The proposed scheme incorporates several noteworthy features: (i) Implementation of a relative threshold event-triggered mechanism to efficiently alleviate communication pressures and computational burdens inherent in the attitude control system. (ii) Utilization of a radial basis function neural network to approximate lumped disturbances, reducing dependence on prior knowledge. (iii) Adaptive compensation for sampling errors and actuator faults by employing the Nussbaum gain. (iv) Integration of a smooth function to address singularity issues and prevent Zeno behavior. Furthermore, Lyapunov analysis validates that all signals within the closed-loop system remain bounded and converge within a predetermined time frame. Comparative numerical simulations underscore the effectiveness and superiority of the proposed control approach. • A novel actuator model mitigates malfunctions and inaccuracies, optimizing controller architecture without discrete handling. • Advanced control allocation and decoupling establish a mapping to adaptively counteract actuator issues and sampling errors with Nussbaum gains. • Fixed-time FT control, independent of initial states, bypasses singularities with a smooth function, avoiding Zeno behavior in backstepping design. [ABSTRACT FROM AUTHOR]

Helicopter formation is characterized by intricate dynamics, complex mechanics, and challenging scenarios, raising the necessity for agile, robust, and convenient controller design. This paper explores the prescribed-time fault-tolerant formation control of multiple helicopters through the incremental fully actuated system approach, which addresses multiple faults in sensors and swash plate struts. First, the helicopter model encompassing aerodynamic, flapping dynamic, swash plate dynamic, actuator faults, and sensor faults is established and further partitioned into attitude and position subsystems. Then, in the attitude subsystem, a prescribed-time attitude controller based on the incremental fully actuated system approach is proposed to track the desired attitude with robustness against actuator faults, aerodynamic drags, inter-axis coupling, and non-linearity. Next, in the position subsystem, the faults of velocity and position sensors are estimated and compensated through the prescribed-time fault-estimation observer, and a prescribed-time formation controller through the incremental fully actuated system approach is designed to achieve distributed leader-follower formation control. A Task-Fault dual-driven scheduling mechanism of the parameters of the prescribed-time technique is developed to promptly activate the prescribed-time function, and the control scheme is proven to be prescribed-time convergent via Lyapunov stability theory. Finally, numerical simulations are conducted to illustrate the efficiency of the algorithm. • Thorough consideration of helicopters' dynamics and faults. • Robustness augmentation to the FASA through an incremental method. • Agility enhancement to helicopter formation with scheduled prescribed-time technique. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven Fault-Tolerant Control (FTC) system based on Model-Free Adaptive Control (MFAC) for a class of nonlinear discrete-time systems. First, the original system is converted in to a Compact Form Dynamic Linearization (CFDL) data model using the Pseudo-Partial-Derivative (PPD) technique. Second, a Rough Neural Network (RNN) is employed as an observer for Fault Detection (FD) by generating residual. Moreover, the obtained residual is incorporated into the CFDL and the optimality criterion to reconstruct the FTC strategy. The key contributions of this research include: 1) Considering and compensating the sensor and actuator faults simultaneously, which improves the overall system's robustness.; 2) the use of RNN as a powerful predictor for noisy and uncertain industrial data for FD, thereby enhancing the accuracy of FD; 3) the direct embedding of the generated residual from RNN into the controller without the need to estimate the fault function, simplifying the control process and 4) the research relies on input and output data from the controlled system for FD and FTC processes, which reduces the computational burden and increases efficiency. Finally, the simulation results show the effectiveness of the proposed data-driven FTC approach in comparision with the existing data-driven FTC approaches in fault control of some sample systems including high-speed train and CSTR system. [ABSTRACT FROM AUTHOR]

This paper focuses on the fault-tolerant load frequency control problem for a multi-area power system with electric vehicles, specifically addressing sensor failures. Electric vehicles are utilized for load frequency control, and a multi-area power system model is established while considering parameter uncertainty. To minimize network data transmission, a memory-based adaptive hybrid event-triggered mechanism is designed, utilizing historical data to construct a threshold function. In addition, both the system states as well as the faults are estimated using a sliding mode observer. The proposed fault-tolerant load frequency control scheme uses observers to mitigate the impact of sensor failures. By applying Lyapunov stability theory, sufficient conditions are derived for the stability of the multi-area power system. Finally, simulations are provided to demonstrate the validity of the proposed schemes. • A multi-area power system model is established for the variation of SOC. • Considering the impact of sensor failures and communication network bandwidth. • The historical state information and adaptive factor are used to construct the ETM. • By constructing multiple Lyapunov functions, the stability criteria are derived. [ABSTRACT FROM AUTHOR]

In this paper, a neuro-adaptive finite-time fault-tolerant control for a class of nonlinear systems is proposed based on the backstepping method, considering model uncertainties and external disturbances. To compensate for model uncertainties, a neural network is employed, while external disturbances and the estimation error of the neural network are handled using a disturbance observer. Additionally, actuator faults are estimated and compensated for distinctly using two adaptive elements in the proposed control system. Moreover, to enhance the learning of the neural network, disturbance observer, and other adaptive elements, particularly in the lack of persistently exciting signals, a composite learning approach is utilized. To this end, a state-observer is introduced, and its estimation error along with the tracking error is used in the updating rules of all the adaptive elements in the design. Additionally, to prevent the explosion of terms in the backstepping method, a command filter with practical finite-time stable auxiliary variables is incorporated into the proposed controller. Finite-time convergence of the tracking error to zero is rigorously proved using the Lyapunov method. To demonstrate the effectiveness of the proposed control method, the problem of attitude control of a rigid spacecraft with moment of inertia uncertainty, external disturbances, and multiple actuator faults is considered since precise attitude control of a spacecraft is of great importance in different maneuvers. The results show that using the introduced control system, the spacecraft is able to track the desired trajectory under different types of uncertain dynamics. [ABSTRACT FROM AUTHOR]

An autonomous fault detection and fault-tolerant control solution combining Model Predictive Control (MPC) and Label Distributed Learning (LDL) is developed for the position tracking and attitude synchronization problems in autonomous satellite rendezvous and docking. The proposed algorithm can deal with center of mass (CM) variations and time-varying mass characteristics due to satellite fuel consumption, actuator saturation and fault, and external disturbances. Firstly, a six-degree-of-freedom (6-DOF) satellite translation-rotation coupling model was established to express the relative motion between the satellite and the target, taking the above factors into account. Subsequently, an on-board autonomous closed-loop fault-tolerant control algorithm using MPC is proposed, in which an LDL-based fault detection mechanism is embedded. The scheme specifies that propulsion faults are estimated by calculating the difference between the MPC's prediction of the satellite's future state and the measured change in the actual state. And the mapping matrix between the commanded force/torque and the actual force of the propulsion system is updated in real time to achieve autonomous fault detection and compensation on board the satellite. Among them, the LDL-based fault detection scheme is trained offline. It requires only simple matrix operations for on-orbit use, which is low-cost and suitable for resource-limited space environments. The additional creation of a time-varying disturbance observer allows the estimation and compensation of unmodelled errors and external disturbances. The stability of the proposed fault-tolerant control algorithm is rigorously demonstrated using Lyapunov analysis. Finally, numerical simulations show that the proposed method can successfully achieve autonomous satellite docking under different combinations of thruster faults. [ABSTRACT FROM AUTHOR]

Large-scale heat pumps are a promising technology for the decarbonisation of heat supplied in buildings and industries, provided they operate as expected. However, common faults like fouling and unplanned downtime periods can significantly affect their performance and availability. This could limit the widespread adoption of large-scale heat pumps over other heating technologies such as gas and electric boilers. Approaches described in the literature to optimize the operation of large-scale heat pumps often lack validation under real-world conditions and do not account for performance degradation due to faults. This work demonstrates a step towards utilizing digital twins to improve the energy performance of a commercial large-scale heat pump affected by fouling. A framework was proposed based on the real-time adaptation of digital twins, where a simulation model was calibrated online based on measurements from the heat pump in operation, which was then used for set point optimization. This enabled to determine optimal intermediate pressure set points in the heat pump operating under varying levels of fouling over time. The framework was tested on different periods of heat pump operation spread over ten calendar months. The results showed that the use of online calibration rather than a single calibration decreased performance estimation errors between 3 and 17 percentage points. Moreover, the set points determined by the online-calibrated model, along with a simpler polynomial model derived from it, showed improvements in the heat pump performance by up to 3%, depending on the level of fouling. The findings of this study demonstrated the potential to extend the proposed framework using digital twins to enhance the energy efficiency of large-scale heat pumps. • Use of a model calibrated online to simulate a two-stage ammonia heat pump. • Online calibration enabled to assess fouling growth and mitigation in real-time. • Intermediate pressure set points were optimized through the online calibrated model. • Online calibration improved performance estimations by 3 to 17 percentage points. • Model-based fouling monitoring was beneficial for the set point optimization. [ABSTRACT FROM AUTHOR]

This article considers a containment control problem for nonlinear multi-agent systems with nonautonomous leaders under directed topology, where each follower may experience various actuator faults and unknown external disturbances. A practical robust fixed-time fault-tolerant controller is proposed that eliminates the adverse effects of disturbances and actuator faults while achieving the containment control objective within a fixed-time. Independent of initial states, a sufficient condition of practical robust fixed-time stabilization is derived, further relaxing the constraint of robust fixed-time stabilization. The combination of sliding-mode control and event-triggered mechanism in this approach provides better control performance. One advantage of this method is that the sliding-mode band size is no longer dependent on the sampling interval but rather on the triggering function. Another advantage is that the resource costs for handling faults are effectively reduced. Additionally, an adaptive boundary layer technique is designed to avoid chattering phenomenon of the discontinuous controller, which utilizes adaptive parameters to autonomously adjust the width of boundary layer. Zeno behavior analysis and a simulated example are also presented. • A sufficient condition for practical robust fixed-time stabilization is derived. • The actuator faults can be efficiently addressed by the containment controller. • The robust fixed-time theory is extended to directed graphs. [ABSTRACT FROM AUTHOR]

This paper studies the distributed attitude synchronization control for flexible spacecrafts with the consideration of unmeasurable modal variable and partial loss of actuator effectiveness faults. The designed rotation matrix-based controller consisted of the basic pattern and the supplementary pattern. Based on a connected undirected graph, the original graph was established on the assumption of inexistent actuator fault, and the unavailable flexible dynamics was approximately described by neural networks (NNs). By using the minimum learning parameter (MLP) algorithm, the finite-time stability of the formation system was improved and the burdensome computation was lightened. A modified sliding mode surface (SMC) is presented which can avoid singularity and the tracking errors can be stabilized to the equilibrium within finite time. • A finite-time attitude synchronization controller for flexible spacecrafts with unknown inertial parameters and actuator faults was proposed. • Actuator fault relation was described by undirected graph and the flexible dynamics was approximated by neural networks (NNs). • Computational complexity of finite-time controller was decreased by using MLP algorithm. [ABSTRACT FROM AUTHOR]

With steadily increasing interest in utilizing wind turbine (WT) systems as primary electrical energy generators, fault-tolerance has been considered decisive to enhance their efficiency and reliability. In this work, an optimal fault-tolerant pitch control (FTPC) strategy is addressed to adjust the pitch angle of WT blades in the presence of sensor, actuator, and system faults. The proposed scheme incorporates a fractional-calculus based extended memory (EM) of pitch angles along with a fractional-order proportional–integral–derivative (FOPID) controller to enhance the performance of the WT. A dynamic weighted parallel firefly algorithm (DWPFA) is also proposed to tune the controller parameters. The efficiency of the proposed algorithm is evaluated on the test functions adopted from 2017 IEEE congress on evolutionary computation (CEC2017). The merits of the proposed fault-tolerant approach are tested on a 4.8-MW WT benchmark model and compared to conventional PI and optimal FOPID approaches. Corresponding comparative simulation results validate the effectiveness and fault-tolerant capability of the proposed control paradigm, where it is observed that the proposed control scheme tends to be more consistent in the power generated at a given wind speed. • A fault-tolerant pitch controller is proposed to improve the power generation of WT. • Fractional-calculus-based memory of the pitch system is merged in the controller. • A modified FA with increased exploitation accuracy and convergence speed is proposed. • The controller parameters are tuned using the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new fault-tolerant operation method of multiphase stacked interleaved Buck converter for green hydrogen production. An improved topology and new fault-tolerant control strategy is proposed. Multi-mode fault-tolerant operation of multiphase stacked interleaved Buck converter for different performance requirements of electrolyzer in hydrogen production system is realized. The multiphase stacked interleaved Buck converter can not only achieve elimination of output current ripple in normal operation mode, but also adopt different fault-tolerant modes according to different demands of electrolyzers in case of failure of any phase of converter. The proposed method is able to improve the availability of multiphase stacked interleaved Buck converter, reduce the times of equipment shutdown, and ensure the continuous operation of hydrogen production system from renewable energy sources. The effectiveness of the proposed method has been verified by experimental results. • An new topology and fault-tolerant control strategy is proposed for green hydrogen production. • The proposal not only achieves the current ripple elimination, but fault-tolerant operation in case of failure. • The proposed solution improves the system operation performance and reliability. • Experimental investigation of the proposed solution is provided. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive fault-tolerant control (FTC) strategy is proposed for a class of high-order fully-actuated (HOFA) systems. A pre-closed-loop treatment is presented to simplify the observer design for the nonlinear system with actuator faults but it leads to a new problem that faults may not satisfy the observation matching conditions. Thus, an intermediate variable observer is introduced to adaptively estimate system states and actuator faults simultaneously. Then the influence of observation errors on system stability is analyzed by analyzing matrix singular value perturbation. Under the proposed adaptive FTC method, the closed-loop global uniformly ultimately bounded stability is achieved by Lyapunov synthesis. Finally, the effectiveness of the proposed FTC method is verified by simulations. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network (NN) constrained fault-tolerant control (FTC) method is proposed for a class of nonlinear pure feedback systems with actuator faults and state constraints. By designing a fault-tolerant compensation controller and a new asymmetric time-varying tangent Barrier Lyapunov function (ATVTBLF), the problem of time-varying asymmetric state constraints caused by actuator failure is solved. The actuator takes into account both loss of effectiveness and bias fault. Different from the existing asymmetric BLF, the new BLF solves the problem of asymmetry under tangent constraints and expands the application range of the asymmetric constraint. NN is used to approximate the unknown items generated in the process of designing fault-tolerant controllers. Based on the Lyapunov stability analysis, it is proved that all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and all states do not violate the bounds. Finally, the simulation example verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this research, a novel control method is proposed for 3-Phase Induction Motor (3-PIM) drives with star-connected under Single-Phase Open-Circuit (SPOC) fault using modified Rotor Field-Oriented Control (RFOC) strategy. The standard RFOC strategy is designed based on the d − q model of 3-PIM during normal mode. This strategy cannot be applied for a 3-PIM under SPOC fault as the model of a healthy 3-PIM is different from the model of a faulty 3-PIM. The use of standard RFOC strategy for Vector Control (VC) of a 3-PIM under SPOC fault increases the electromagnetic torque ripples during this fault. To overcome this problem, two unbalanced rotational transformations are used. A Current Transformation Matrix (CTM) is obtained based on the concept of maintaining value of Magnetic Motive Force (MMF) of the faulted machine like the healthy machine. Then, a Voltage Transformation Matrix (VTM) is introduced by using the presented CTM and according to the Single-Phase Induction Motor (SPIM) model. Based on the results, faulted machine RFOC equations were obtained similar to the healthy machine RFOC equations by using the CTM and VTM. The proposed technique was verified by simulations and experiments. Results indicated that the performances of the faulted machine under the proposed RFOC strategy are better than standard control techniques. • A modified RFOC strategy for star-connected 3-PIMs under open phase fault has been presented. • The overall performances of the basic RFOC system during open-phase fault have been improved. • A simple and accurate FTC system in comparison with other previous FTC schemes has been proposed. • The introduced control technique can be used to control SPIM drive systems. [ABSTRACT FROM AUTHOR]

A sliding mode based fault-tolerant control method using neural learning is studied for hypersonic reentry vehicle (HRV) in this paper. Based on the non-singular second-order terminal sliding mode, the composite neural learning is adopted to deal with the system uncertainties caused by the additive faults. The main point is to construct the prediction error to evaluate the performance of intelligent approximation. Meanwhile, a disturbance observer is employed to estimate the unknown disturbance while the actuator multiplicative fault is compensated with an adaptive law. The simulation of HRV is conducted to verify the effectiveness of the proposed fault-tolerant control scheme. [ABSTRACT FROM AUTHOR]

This paper provides two bounded finite-time control strategies for the stabilization problem of Euler–Lagrange (EL) systems exposed to actuator failures. By the combination of sliding mode technology and adaptive method, two control architectures have been constructed such that the system states will be forced towards the origin within finite time. The first controller is applicable in the case that exact information of the external disturbance and actuator faults are available. However, it still remains challenging for designers to obtain this sort of information in engineering practice. On account for this, adaptive laws are applied in the second controller to obtain estimations about the unknown parameters, thus ensuring desirable fault-tolerance ability and robustness for the EL systems. By resorting to the properties of EL systems and hyperbolic tangent functions, outputs of these two controllers will be proven to be bounded. Simulation examples are presented to manifest the validity of the developed algorithms. [ABSTRACT FROM AUTHOR]

Under the detrimental effects of sensor and actuator faults, the blade pitch system is found to be the least reliable subsystem. Therefore, to apprehend required level of reliability and efficiency, an efficient Fault Tolerant Control framework (FTC) seems crucial. This paper presents an Active FTC (AFTC) strategy to control the pitch angle of a wind turbine in the presence of actuator and sensor faults, uncertainties and exogenous disturbances. First, a lumped term consisting of model uncertainty and disturbance is estimated by a novel dual layer adaptive uncertainty estimator. Next, to achieve high accuracy in supplying the required power, a continuous adaptive time delay control is designed based on the estimated uncertainties. In the proposed controller, chattering caused by discontinuous control term is eliminated, and faults are accommodated. Stability of the closed-loop system is demonstrated by the Lyapunov theory. Furthermore, in order to verify the validity of the proposed strategy, the controller is implemented in FAST-MATLAB/Simulink for five different load cases generated using TurbSim. Results confirm the effectiveness and superiority of the proposed structure in the presence of sensor and actuator faults (bias, gain, performance degradation and actuator stuck) compared to Nonlinear PI (N-PI) control and Feedback Linearized Control (FLC) schemes. [ABSTRACT FROM AUTHOR]