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This paper reviews the application of Hall position sensors in control and position estimation of permanent magnet synchronous motors (PMSMs). Accurate rotor position and motor speed data are essential for the high-efficiency control of PMSMs in modern industry. Rotor position and motor speed can be measured by mechanical position sensors, which are costly and less reliable, or by rotor position observers, which are sensitive to system models and changes in motor parameters. This paper examines the benefits, limitations, challenges, and uses of low-resolution Hall position sensors in PMSM drives, presenting them as a cost-effective solution for achieving a balance between performance and expense. In addition, the paper discusses recent solutions to issues related to misplaced Hall position sensors and fault-tolerant control algorithms, and gives an outlook to future developments in this area. [ABSTRACT FROM AUTHOR]

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]

In practice, the time-varying communication delays and actuator failures are the main inevitable issues in nonlinear multilateral teleoperation systems, which can reduce the performance and stability of the considered systems. This article proposed a novel failure-distribution-dependent H ∞ fuzzy fault-tolerant control scheme to realize position synchronization and force tracking simultaneously for multilateral teleoperation systems. Firstly, the nonlinear multilateral systems were modeled as a kind of T-S fuzzy systems with multiple time-varying delays. Then, based on the distribution characteristic of failures, by introducing a series of tradeoff coefficients, a novel failure-distribution-dependent fault-tolerant control algorithm was provided to ensure force tracking in spite of failures, and the purpose of position synchronization was achieved (not only the master and slave robot position synchronization but also the position synchronization between each slave robot). Finally, a numerical simulation example was given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This work investigates the prescribed finite-time event-triggered control of uncertain nonlinear systems with fullstate constraints and actuator failures. In order to approximate the unknown nonlinear dynamic function, the fuzzy logic system (FLS) is used in this paper. By integrating the backstepping design framework with Lyapunov stability theory, a novel eventtriggered condition that incorporates a decreasing function of the system interstate error is proposed. The developed method not only confines tracking errors within a predetermined range within the prescribed setting time but also ensures that all states adhere to their respective constraints. Additionally, the approach effectively mitigates the impacts of the Zeno phenomenon and actuator failure. Simulation results on a massspring-damper system demonstrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates hall sensor signal unbalancing faults in sensored Brushless Direct Current (BLDC) motor drives and presents a fault effect minimization scheme for implementation in motor controller without any modification in system architecture, or core commutation method. In the proposed scheme detection, classification, and estimation of fault extent are carried out separately at the start of controller itself for subsequent corrections without involving issue of repetitive error estimation and finding valid reference signal separately for correcting inverter switching mistiming, as required in classical methods. The categorization part of scheme maps the faults with corresponding hall sensors. The proposed scheme is validated in MATLAB/Simulink model. It is tested on experimental test bench of controller and 1 kW BLDC motor of an electric rickshaw, along with another technique based on least square method (LSM) for performance comparison. Results show reduction of peak-to-peak value by 30% and ripple content by 50% in phase currents, and peak-to-peak reduction of 47% with 29% lesser ripple content in DC link current by using the presented scheme. The observed increment in efficiency is 0.8% and 2% in nominal torque/ampere at 30% load. At 100% load, increase of 2.2% efficiency and 9% nominal torque/ampere are observed. The increased memory loading of controller is estimated to be 1.25 times as compared to normal run without any correction scheme. Approximately 24% of this additional loading is utilized for repetitive calculations as compared to 63% in the other LSM based scheme, resulting in lesser run time loading of controller. The transient response with sudden load change shows better response with implementation of the proposed scheme in controller. [ABSTRACT FROM AUTHOR]

Manufacturing systems are becoming more sophisticated and expensive, particularly with the development of the intelligent industry. The complexity of the architecture and concept of Smart Manufacturing (SM) makes it vulnerable to several faults and failures that impact the entire behavior of the manufacturing system. It is crucial to find and detect any potential anomalies and faults as soon as possible because of the low tolerance for performance deterioration, productivity decline, and safety issues. To overcome these issues, a variety of approaches exist in the literature. However, the multitude of techniques make it difficult to choose the appropriate method in relation to a given context. This paper proposes a new architecture for a conceptual model of intelligent fault diagnosis and self-healing for smart manufacturing systems. Based on this architecture, a review method for the different approaches, sub-approaches and methods used to develop a Fault Detection and Diagnosis (FDD) and Self-Healing-Fault-Tolerant (SH-FT) strategy dedicated to smart manufacturing is defined. Moreover, this paper reviews and analyzes more than 256 scientific articles on fault diagnosis and self-healing approaches and their applications in SM in the last decade. Finally, promising research directions in the field of resilient smart manufacturing are highlighted. [ABSTRACT FROM AUTHOR]

A fault-tolerant control approach is proposed, for a pumping airborne wind energy system (AWES) comprising a tethered fixed-wing aircraft with integrated propellers for vertical take-off and landing (VTOL). First, the flight control design for the traction phase of the system, when the tethered aircraft has to fly in loops using the rudder, is presented. Then, the presence of the propellers, that are normally not used in the traction phase, is exploited to obtain a fault tolerant controller in case of rudder malfunctioning. The approach detects a possible discrete control surface fault and compensates for the loss in actuation by using the VTOL system. A sophisticated model of the system is used to analyse the performance of the proposed technique. The main finding is that the approach is able to handle abrupt rudder faults with high tolerance. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to address the challenge of designing finite-time fault-tolerant control mechanisms for nonlinear flexible spacecraft systems, which are particularly vulnerable to randomized actuator faults. Diverging from traditional methodologies, our research harnesses the capabilities of the Takagi–Sugeno (T–S) fuzzy framework. A unique feature of our model is the representation of actuator failures as stochastic signals following a Markov process, thereby offering a robust solution for addressing timeliness concerns. In this paper, we introduce a generalized reciprocally convex inequality that includes adjustable parameters, broadening the scope of previous results by accommodating them as special cases. Through the amalgamation of this enhanced inequality and flexible independent parameters, we propose an innovative controller design strategy. This approach establishes a stability standard that guarantees mean-square H ∞ performance. In order to validate the efficacy of the suggested strategy, we present a numerical illustration involving a nonlinear spacecraft system, showcasing the practical advantages and feasibility of our proposed technique. [ABSTRACT FROM AUTHOR]

Failure of any of the drive systems in a Four-Wheel Independent Drive (4WID) electric vehicle may affect the control performance and driving safety of the whole vehicle. Therefore, in this paper, a fault-tolerant controller (FTC) for 4WID electric vehicles considering drive actuator failures is proposed. First, a comprehensive characterization of multiple fault types is achieved by establishing a generalized fault model and designing a comprehensive fault factor. Second, based on the comprehensive fault factor, an LPV model with faults is constructed. Further, a fault-tolerant controller based on LPV/H∞ output feedback is designed by combining the weighting function. Finally, the effectiveness of the FTC in this paper is verified by simulation and hardware-in-the-loop (HIL) experiments. The experimental results show that the FTC designed in this paper can improve the stability of the vehicle traveling while ensuring tracking accuracy when the drive system fails. [ABSTRACT FROM AUTHOR]

This article investigates a heterogeneous vehicular platoon control problem, in which prescribed tracking performance can be achieved, it is assumed that the vehicle subjects to asymmetric nonlinear actuator saturation, dead-zone nonlinearity and actuator faults. Based on improved exponential spacing policy, an adaptive fault-tolerant sliding-mode control scheme is proposed to guarantee individual vehicle stability, string stability and traffic flow stability. The hypothesis that the spacing errors at initial time are zero is removed by employing the novel exponential spacing policy. Furthermore, to attenuate the harmful effects of actuator faults, saturation and dead-zone nonlinearity, a compensation system based on radial basis neural network (RBFNN) is established. Finally, the effectiveness of the proposed control scheme is verified by simulation results. [ABSTRACT FROM AUTHOR]

To enhance the reliability and self-healing of the system, the research on fault tolerance of the reconfigurable modular centralized control center is its development trend. Most of the previous research has focused on hardware redundancy. Improving fault tolerance performance is an essential topic in the research of centralized control platforms. Firstly, the problem of centralized fault tolerance in the working configuration of a reconfigurable manipulator is studied. The effect of each hinge on fault tolerance in the existing configuration is studied with the criterion of manoeuvrability and tolerable space. The fault module was first modelled to represent the system architecture information. A modular motion rule based on autonomous recombination technology is proposed. A self-organizing deformation algorithm with fault tolerance is studied. The fault tolerance of the motion pairs is compensated by adding a small number of motion pairs to ensure the configuration characteristics. With the addition of a failure compensation device, the joint's range of motion was reduced, and the fault tolerance rate was enhanced. After the failure of the robot arm, the fault tolerant control method can still ensure that the robot arm can perform work in its tolerable working space. The test results show that the fault tolerance analysis method is practical and feasible. It lays a theoretical foundation for the application in aerospace, industry and other fields. [ABSTRACT FROM AUTHOR]

This paper investigates an L1 adaptive fault-tolerant control scheme for nonlinear systems with input constraint, external disturbances, and multiple faults, which include actuator faults and sensor faults. Faults and input constraint are important factors that affect the stability and performance of a control system. Actuators and sensors are the most vulnerable components, with the former receiving more attention in comparison. In this paper, sensor faults are first transformed into pseudo-actuator faults through the augmented matrix approach, which facilitates their handling together with actuator faults. Saturation constraints on the control signal are not conducive to the design of the controller. The conversion of an input-saturated function to a time-varying linear system is completed based on function approximation and Lagrange's mean value theorem. Moreover, a nonlinear system with unknown input gain and uncertainties is constructed using these methods. Next, an L1 adaptive fault-tolerant controller is designed to cope with uncertainties, including system uncertainties, external disturbances, faults, and approximation errors. In the L1 adaptive controller, the online estimation of the time-varying parameters allows for updating of the system state, while the combination of the two is transmitted to the control law such that it can compensate for the effects of the uncertainties. The stability and performance boundaries are further derived using the Lyapunov theory and the L1 reference system. Finally, simulations are carried out to demonstrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

In this paper, adaptive fuzzy fault-tolerant control (AFFTC) for the attitude control system of a hypersonic gliding vehicle (HGV) experiencing an actuator fault is proposed. Actuator faults of the HGV are considered with respect to its actual structure and actuator characteristics. The HGV's attitude system is firstly represented by a T–S fuzzy model, and then a normal T–S fuzzy controller is designed. A reinforcement learning (RL)-based policy iterative solution algorithm is proposed for the solving of the T-S fuzzy controller. Then, based on the normal T–S controller, a fuzzy FTC controller is proposed in which the control matrices can improve themselves according to the special fault. An integral reinforcement learning (IRL)-based solving algorithm is proposed to reduce the dependence of the design methods on the HGV model. Simulations on three different kinds of actuator faults show that the designed IRL-based FTC can ensure a reliable flight by the HGV. [ABSTRACT FROM AUTHOR]

In the speed-sensorless vector control of induction motors (IMs), the speed estimation accuracy suffers from the deteriorated current measurement caused by the current sensor faults, such as open circuit in one phase, DC bias, and odd harmonics. In this paper, a novel speed estimation strategy based on the current sensor fault-tolerant control is proposed to improve the speed estimation accuracy under the current sensor faults. First, to detect the current sensor faults in real time, the sequential probability ratio test is introduced to the system by using the innovations of the extended Kalman filter (EKF). Second, to ensure speed estimation accuracy, a double-cascading second-order generalized integrator (DSOGI) is employed to reconstruct the faulty current information when a fault is identified. Finally, the reconstructed current information is fed back to the sequential probability extended Kalman filter (SPEKF), which estimates the rotor speed of the IM, and high-accuracy speed estimation under the condition of current sensor faults is achieved. The effectiveness of the proposed strategy is validated by a series of experiments, which were conducted on a 3 kW induction motor drive platform. [ABSTRACT FROM AUTHOR]

Abstract This paper introduces a novel data‐driven approach to develop a fault‐tolerant model predictive controller (MPC) for non‐linear systems. By adopting a Koopman operator‐theoretic perspective, the proposed method leverages historical data from the system to construct a data‐driven model that captures the non‐linear behaviour and fault characteristics. The fault influence is addressed through an online estimation of a time‐varying Koopman predictor, which allows for adjusting the MPC control law to counteract the fault effects. This estimation is performed in a higher dimensional Koopman feature space, where the dynamics behave linearly. As a result, the non‐linear fault‐tolerant MPC optimization problem can be replaced with a more practical and feasible linear time‐varying one using the approximated Koopman predictor. Moreover, by incorporating the online update procedure, the time‐varying Koopman predictor can represent the dynamics of the faulty system. Hence, the controller can adapt and compensate for the faults in real‐time, integrating the fault diagnosis module in the MPC framework and eliminating the need for a separate fault detection unit. Finally, the efficacy of the proposed approach is demonstrated through case study results, which highlight the ability of the controller to mitigate faults and maintain desired system behaviour.

Abstract This paper addresses the problem of achieving finite‐time fault‐tolerant consensus control for a class of non‐linear fractional‐order multi‐agent systems (NFO‐MAS) using finite‐time fault detection and estimation, as well as a finite‐time state observer. To achieve this, a specific lemma is utilized to rewrite the high‐order model of NFO‐MAS as a lower‐order NFO unique system. By employing new identification rules and introducing a fault estimation method, both the state variables and faults of the agents are estimated within a finite time. Subsequently, a finite‐time sliding mode control law is designed based on the estimated fault and the state variables obtained from the proposed finite‐time observer to achieve consensus within a finite time for the fractional‐order non‐linear MAS. The stability of the fault estimation, state observer, and consensus controller is proven using the finite‐time Lyapunov theory. The effectiveness of the proposed approach is demonstrated through numerical simulations.

This paper presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The aim of this work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. For this purpose, a fault detection and diagnosis system (FDD) based on a moving window principal component analysis technique (MWPCA) will be developed to provide an online fault monitoring solution for large-scale complex processes (e.g., sewer systems) with dynamically changing characteristics, and a reconfiguration algorithm for the MPC controller taking advantage of its own features such as constraint handling. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end. [ABSTRACT FROM AUTHOR]

This paper introduces a robust fault-tolerant control (FTC) for a five-phase permanent magnet synchronous motor (FPPMSM) affected by the third harmonic under an open-circuit fault (OCF). Using field-oriented control, the proposed method demonstrates how to achieve optimal current references for torque decoupling under healthy and faulty conditions. The proposed speed and current loop controllers are based on sliding mode control (SMC), with a nonlinear extended state observer (NESO) that utilizes a hyperbolic tangent function (HTF) to provide feed-forward compensation to the controllers. The results analysis confirmed that the proposed control could enhance the tracking accuracy and robustness to disturbances under various conditions, substantially reducing torque ripples and speed fluctuations under a fault. [ABSTRACT FROM AUTHOR]

In the conventional dual-loop vector control strategy of Voltage Source Converter-based High Voltage Direct Current (VSC-HVDC) systems employed in offshore wind farms, challenges such as complex PI parameter-tuning and slow response speed exist. Furthermore, a single-phase bridge-arm fault in the converter station can lead to a change in system parameters, resulting in the failure of the original control strategy. Hence, this paper proposes a fault-tolerant control strategy for grid-connected offshore wind farms, based on model predictive control (MPC). Firstly, the predictive models for both normal and fault-tolerant states of the grid-side converter station are established based on the system structure of the grid-side converter station and a super-local model. Subsequently, a cost function is constructed using the power error, with the optimization objective set as the value function. This approach allows for accurate prediction of the future switching states of the grid-tied inverter to track the reference power. Finally, a simulation model of the offshore wind power grid system is established in the MATLAB/Simulink (2022a) environment. The results demonstrate that the grid-side converter station can effectively operate in a fault-tolerant manner under the proposed control strategy, thereby enhancing the disturbance resistance and fault-recovery capabilities of the offshore wind VSC-HVDC system. [ABSTRACT FROM AUTHOR]

This paper proposes a fault-tolerant control (FTC) strategy using the current space vectors to diagnose sensor failures and enhance the sustained operation of a field-oriented (FO) controlled induction motor drive (IMD). Three space vectors are established for the sensor fault diagnosis technique, including one converted from the measured currents and the other two calculated from the current estimation technique, respectively, measured and with reference speeds. A mixed mathematical model using three space vectors and their components is proposed to accurately determine the fault condition of each sensor in the motor drive. After determining the operating status of each sensor, if the sensor signal is in good condition, the feedback signal to the controller will be the measured signal; otherwise, the estimated signal will be used instead of the failed signal. Failure states of the various sensors were simulated to check the effectiveness of the proposed technique in the Matlab/Simulink environment. The simulation results are positive: the IMD system applying the proposed FTC technique accurately detected the failed sensor and maintained stability during the operation. [ABSTRACT FROM AUTHOR]

Submerged aquatic vegetation plays a fundamental role as a habitat for the biodiversity of marine species. To carry out the research and monitoring of submerged aquatic vegetation more efficiently and accurately, it is important to use advanced technologies such as underwater robots. However, when conducting underwater missions to capture photographs and videos near submerged aquatic vegetation meadows, algae can become entangled in the propellers and cause vehicle failure. In this context, a neurobiologically inspired control architecture is proposed for the control of unmanned underwater vehicles with redundant thrusters. The proposed control architecture learns to control the underwater robot in a non-stationary environment and combines the associative learning method and vector associative map learning to generate transformations between the spatial and velocity coordinates in the robot actuator. The experimental results obtained show that the proposed control architecture exhibits notable resilience capabilities while maintaining its operation in the face of thruster failures. In the discussion of the results obtained, the importance of the proposed control architecture is highlighted in the context of the monitoring and conservation of underwater vegetation meadows. Its resilience, robustness, and adaptability capabilities make it an effective tool to face challenges and meet mission objectives in such critical environments. [ABSTRACT FROM AUTHOR]

The paper introduces a robust adaptive fault-tolerant control system for the six-degree-of-freedom (six-DOF) dynamics of quadrotor unmanned aerial vehicles (UAVs), incorporating disturbances and abrupt actuator faults to represent real-world conditions. The proposed control scheme employs robust control terms to manage unknown disturbances. However, robust control performance may degrade due to sudden fault impacts. To handle this issue, we introduce adaptive laws to ensure continuous adaptation. The control architecture ensures the tracking system's stability by combining robust control using sliding-mode control (SMC) with adaptive control developed using the certainty equivalence principle. The sliding-surface error limits the adaptive laws, in which the convergence of estimated parameters to the actual unknown variables is not required as they fully rely on the convergence of the tracking error. We provide rigorous mathematics to validate the proposed control design. Furthermore, we conduct numerical simulations for a quadrotor UAV to showcase the effectiveness of the proposed scheme. The results demonstrate the efficacy of the proposed design in handling external disturbances and abrupt actuator faults. [ABSTRACT FROM AUTHOR]

It is always problematic that the initial value of the trajectory tracking error must be inside the area included in the prescribed performance constraint function. To overcome this problem, a novel fault-tolerant control strategy is designed for a second-order multi-input and multi-output nonlinear system (MIMO-NLS) with unknown initial states, actuator faults, and control saturation. Firstly, a predefined time convergence (PTC) stability criterion is theoretically proven. Then, an error conversion function is introduced to convert the trajectory tracking error to a new error variable with an initial value of zero, and an adaptive fuzzy system is designed to approximate the compound interference composed of actuator fault, parameter perturbation, control saturated overamplitude, and external disturbance. Based on the backstepping control method, prescribed performance control method, and predefined time convergence stability theory, an adaptive fuzzy fault-tolerant controller for the new error variable is designed and theoretically proven for the predefined time convergence of the closed-loop system. The numerical simulation results of the guaranteed performance trajectory tracking control for industrial robots with actuator faults demonstrate that the adaptive fuzzy fault-tolerant control algorithm has strong fault tolerance to actuator faults and anti-interference capabilities. The convergence time and performance of trajectory tracking errors can be preset in advance, and the parameter settings of the prescribed performance constraint function are not affected by the initial state values. [ABSTRACT FROM AUTHOR]

This study addresses the problem of cooperative control design for a group of car-like vehicles encountering fading channels, actuator faults, and external disturbances. It is presumed that certain followers lack direct access to the states of the leader via a directed graph. This arises challenges in maintaining synchronization and coordination within the network. The proposed control strategy utilizes non-singular fast terminal sliding mode control to accelerate consensus tracking and enhance the convergence of the overall system. This controller is designed to mitigate the impact of actuator faults in the presence of fading channels in the communication network. The effects of such issues on team performance are rigorously analyzed. Based on the Lyapunov stability principle, it has been demonstrated that the controller is capable of providing satisfactory performance for the entire system despite these challenges. Moreover, vehicle synchronization can be effectively maintained. Numerical simulations are conducted to verify the theoretical findings. [ABSTRACT FROM AUTHOR]

This study presents a novel image-based visual servoing fault-tolerant control strategy aimed at ensuring the successful completion of visual servoing tasks despite the presence of robotic arm actuator faults. Initially, a depth-independent image-based visual servoing model is established to mitigate the effects of inaccurate camera parameters and missing depth information on the system. Additionally, a robotic arm dynamic model is constructed, which simultaneously considers both multiplicative and additive actuator faults. Subsequently, model uncertainties, unknown disturbances, and coupled actuator faults are consolidated as centralized uncertainties, and an iterative learning fault observer is designed to estimate them. Based on this, suitable sliding surfaces and control laws are developed within the super-twisting sliding mode visual servo controller to rapidly reduce control deviation to near zero and circumvent the chattering phenomenon typically observed in traditional sliding mode control. Finally, through comparative simulation between different control strategies, the proposed method is shown to effectively counteract the effect of actuator faults and exhibit robust performance. [ABSTRACT FROM AUTHOR]

In recent years, robot manipulator arms have become increasingly prevalent and are playing pivotal roles across various industries. Their ability to replace human labor in arduous and hazardous tasks has positioned them as indispensable assets. Consequently, there has been a surge in research efforts aimed at enhancing their operational performance. The imperative to improve their efficiency and effectiveness has garnered significant attention within the research community. In this study, a novel fault-tolerant control (FTC) scheme for robot manipulators to handle the effects of the unknown input is proposed to aid robots in achieving good tracking performance. In the first step, an extended state observer (ESO) is constructed to approximate both velocities and the unknown input in the robot system. The observer offers estimation information with good accuracy and quick convergence. The estimated signals are then combined with computed torque control (CTC), which is a useful control technique for trajectory tracking of robot manipulator systems, to construct an active FTC to decrease the influences of the unknown input. The proposed algorithm does not require velocity measurement in the design process. In addition, with a novel design approach, the combination of controller and observer provides a novel control signal that delivers higher tracking performance compared to the traditional design approach. The global and asymptotic stability of the suggested technique is proved through the Lyapunov theory. Finally, simulations are implemented on a 2-degree-of-freedom (DOF) robot manipulator to validate the efficiency of the proposed controller–observer method. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicles (AUVs) play a significant role in ocean-related research fields as tools for human exploration and the development of marine resources. However, the uncertainty of the underwater environment and the complexity of underwater motion pose significant challenges to the fault-tolerant control of AUV actuators. This paper presents a fault-tolerant control strategy for AUV actuators based onTakagi and Sugeno (T-S) fuzzy logic and pseudo-inverse quadratic programming under control constraints, aimed at addressing potential actuator faults. Firstly, considering the steady-state performance and dynamic performance of the control system, a T-S fuzzy controller is designed. Next, based on the redundant configuration of the actuators, the propulsion system is normalized, and the fault-tolerant control of AUV actuators is achieved using the pseudo-inverse method under thrust allocation. When control is constrained, a quadratic programming approach is used to compensate for the input control quantity. Finally, the effectiveness of the fuzzy control and fault-tolerant control allocation methods studied in this paper is validated through mathematical simulation. The experimental results indicate that in various fault scenarios, the pseudo-inverse combined with a nonlinear quadratic programming algorithm can compensate for the missing control inputs due to control constraints, ensuring the normal thrust of AUV actuators and achieving the expected fault-tolerant effect. [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]

The unmanned surface vessel (USV) is an emerging marine tool with its advantages of automation and intelligence in recent years; the good trajectory tracking performance is an important capability. This paper proposes a novel prescribed performance fixed-time fault-tolerant control scheme for an USV with model parameter uncertainties, unknown external disturbances, and actuator faults, based on an improved fixed-time disturbances observer. Firstly, the proposed observer can not only accurately and quickly estimate and compensate the lumped nonlinearity, including actuator faults, but also reduce the chattering phenomenon by introducing the hyperbolic tangent function. Then, under the framework of prescribed performance control, a prescribed performance fault-tolerant controller is designed based on a nonsingular fixed-time sliding mode surface, which guarantees the transient and steady-state performance of an USV under actuator faults and meets the prescribed tracking performance requirements. In addition, it is proved that the closed-loop control system has fixed-time stability according to Lyapunov's theory. Finally, upon conducting numerical simulations and comparing the proposed control scheme with the SMC and the finite-time NFTSMC scheme, it is evident that the absolute error tracking performance index of the proposed control scheme is significantly lower, thus indicating its superior accuracy. [ABSTRACT FROM AUTHOR]

This paper investigates decentralized adaptive event-triggered fault-tolerant control for interconnected nonlinear delay systems with actuator failures. The actuator failures suffered include loss of effectiveness and bias faults. A control scheme based on the K-filter is proposed, which effectively compensates for the effects of unknown actuator failures. A hyperbolic tangent function and neural network are introduced to approximate the unknown interconnection function and nonlinear delay function. By introducing the dynamic surface control method, the "explosion of complexity" issue is addressed. Furthermore, our proposed controller can ensure that all states of the corresponding closed-loop system are semi-globally uniformly ultimately bounded and that the tracking error can converge to a small neighborhood of zero. Meanwhile, Zeno behavior can be effectively avoided. Finally, the validity of the proposed control scheme is verified using a simulation example. [ABSTRACT FROM AUTHOR]

A new approach for output feedback L 1 adaptive control based on a proportional adaptation law is presented. The effectiveness of this design is assessed in simulation and validated through real-time testing on an airfoil pitch control wind tunnel experimental rig. Experimental evaluation of the robustness of the controllers, assessed by introducing various disturbances into the control signals, shows that the adaptive control has a better performance compared to PID control, particularly in scenarios with reduced control effectiveness and time-varying disturbances. The experimental results demonstrate the efficacy of the proposed method in practical applications. [ABSTRACT FROM AUTHOR]

This paper introduces a novel data‐driven approach to develop a fault‐tolerant model predictive controller (MPC) for non‐linear systems. By adopting a Koopman operator‐theoretic perspective, the proposed method leverages historical data from the system to construct a data‐driven model that captures the non‐linear behaviour and fault characteristics. The fault influence is addressed through an online estimation of a time‐varying Koopman predictor, which allows for adjusting the MPC control law to counteract the fault effects. This estimation is performed in a higher dimensional Koopman feature space, where the dynamics behave linearly. As a result, the non‐linear fault‐tolerant MPC optimization problem can be replaced with a more practical and feasible linear time‐varying one using the approximated Koopman predictor. Moreover, by incorporating the online update procedure, the time‐varying Koopman predictor can represent the dynamics of the faulty system. Hence, the controller can adapt and compensate for the faults in real‐time, integrating the fault diagnosis module in the MPC framework and eliminating the need for a separate fault detection unit. Finally, the efficacy of the proposed approach is demonstrated through case study results, which highlight the ability of the controller to mitigate faults and maintain desired system behaviour. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of achieving finite‐time fault‐tolerant consensus control for a class of non‐linear fractional‐order multi‐agent systems (NFO‐MAS) using finite‐time fault detection and estimation, as well as a finite‐time state observer. To achieve this, a specific lemma is utilized to rewrite the high‐order model of NFO‐MAS as a lower‐order NFO unique system. By employing new identification rules and introducing a fault estimation method, both the state variables and faults of the agents are estimated within a finite time. Subsequently, a finite‐time sliding mode control law is designed based on the estimated fault and the state variables obtained from the proposed finite‐time observer to achieve consensus within a finite time for the fractional‐order non‐linear MAS. The stability of the fault estimation, state observer, and consensus controller is proven using the finite‐time Lyapunov theory. The effectiveness of the proposed approach is demonstrated through numerical simulations. [ABSTRACT FROM AUTHOR]

This paper addresses the issues of maximum power point tracking (MPPT) and fault-tolerant control in tidal steam turbines under complex marine environments. In order to solve the conflicting problems in the existing sliding mode control between dynamic performance and chatter reduction as well as the use of fault estimation link in the fault-tolerant control, which increases the system complexity, an adaptive non-singular fast terminal sliding mode and adaptive robust fault tolerance method (ANFTSMC-ARC) is proposed. First, a speed controller equipped with adaptive non-singular fast terminal sliding mode control (ANFTSMC) is designed to improve the power capture efficiency under swell disturbances. This design achieves fast convergence and circumvents the singularity problem. Then, a new reach law is proposed based on the adaptive hybrid exponential reaching law (AHERL), which ensures high tracking performance while reducing chattering. In addition, considering that the hydraulic pitch system is prone to failure, a fault-tolerant controller with automatically adjustable gain is designed under the adaptive robust scheme. With the help of Lyapunov theory, the closed-loop system is proved to be uniform and ultimately bounded. Finally, comparative simulation results verify the efficiency of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This work mainly studies the issue of predefined time and accuracy adaptive fault-tolerant control for strict-feedback nonlinear systems with multiple faults. The faults in the controlled system include actuator faults and external system faults. The unknown functions for nonlinear systems are approximated by fuzzy logic systems (FLSs). And then, according to the backstepping technique and the predefined time stability theory, an adaptive fuzzy control algorithm is presented, which can make sure that all closed-loop system signals remain predefined time bound and the tracking error converges to a predefined accuracy within the predefined time. Ultimately, the effectiveness of the presented control algorithm is proved through two simulation examples. [ABSTRACT FROM AUTHOR]

To ensure the three-level neutral point clamped (NPC) grid-connected inverter in a grid-connected system continuous operation after a single-phase bridge arm short circuit or open circuit failure, an optimal compensation fault-tolerant control strategy with low common-mode voltage is proposed in this paper, which is based on space vector pulse width modulation (SVPWM). Firstly, the reference voltage vector synthesis rule is determined by analyzing the common mode voltage corresponding to the switch state of the eight-switch three-phase inverters (ESTPI), which is the topology of the three-level NPC inverter with one phase failure. Then, the mechanism of neutral point potential fluctuation is analyzed by the neutral point current change in a fundamental wave period. Further, the space vector synthesis is compensated based on this mechanism. Finally, the low-pass filter and hysteresis controller are designed to optimize vector synthesis compensation, so as to ensure the quality of grid-connected current and effectively restrain the neutral point potential offset of the DC bus. The simulation results show that the proposed fault-tolerant control strategy can realize the stable and reliable operation of the grid-connected system after single-phase bridge arm failure, and the common-mode voltage can be reduced for one-third of the period time. The quality of grid-connected current is improved significantly. The proposed controller has good control characteristics when the grid-connected current changes.

The main drive system of the rolling mill often experiences torsional vibrations, which severely affect product quality, precision, and the service life of the transmission equipment. This paper investigates the torsional vibration suppression problem in the main drive system of the rolling mill, considering actuator faults, nonlinear friction, nonlinear damping, and model uncertainties. Based on the high-order fully actuated (HOFA) system approach, the main drive system of the rolling mill is transformed into a rolling mill main drive fully actuated system (RMMDFAS). Adaptive neural networks are introduced to address unknown uncertainties, and a continuous differentiable Gaussian error function is used to handle actuator faults. An adaptive neural network fault-tolerant control law for motor torque is proposed. The stability of the designed main drive torsional vibration system is rigorously proven, while maintaining the performance of the transformed states. Finally, the effectiveness and superiority of the proposed algorithm are verified through simulations.

Because of their attractive features, switched reluctance motors (SRMs) are strong candidates with real chances on the market for several industrial applications including vehicle propulsion. However, the SRMs suffer from torque ripple and associated acoustic noise. A proper control algorithm can handle torque ripples to an accepted limit. However, the internal faults in stator windings of SRMs deteriorate not only the performance of control algorithm but also the output power, efficiency, and noise (torque ripple). They cause fast degradation of motor lifetime. Therefore, this paper introduces a novel method to counteract the internal faults in winding of SRMs. A new simulation method for SRMs including the capability of interturn faults is introduced. The proposed novel interturn fault tolerant control (FTC) is achieved based on average torque control (ATC) algorithm. The switching angles are optimized smartly providing a less complicated control algorithm to fit properly with the industrial platforms. The simplicity of overall control algorithm is still functional. The performance of proposed FTC is evaluated compared to healthy motor conditions as well as faulty conditions. The results show the superior performance of the proposed control.

This study focuses on fault-tolerant finite-time flight-path angle control for an aircraft in the presence of external disturbances, unknown deadzone and actuator fault. To begin with, the longitudinal model of aircraft is introduced for the subsequent controller design. Then, a new smooth deadzone inverse model is presented to compensate for the deadzone nonlinearity in aircraft system. A robust adaptive fault-tolerant finite-time control law is derived by using backstepping adaptive control approach, where a finite-time stability criterion is adopted for developing practical finite-time control. In order to reduce the difficulty of control system design, two finite-time differentiators are used to estimate the derivatives of virtual control signals. The coupling errors of the deadzone and faults are properly dealt with by estimating the unknown bounds. Finally, comparative numerical simulation results are provided to demonstrate the efficiency of the proposed fault-tolerant finite-time flight-path angle control scheme.

This paper proposes a robust sampled-observer-based fault-tolerant min-type switching law for a class of continuous-time uncertain switched affine systems (SAS) subject to persistent bounded sensor faults using Luenberger observers to estimate the system state. The switching law is designed to make the trajectories of the system be attracted to an open ball containing a given equilibrium point, even in the presence of faults and norm-bounded uncertainties. First, nonlinear matrix conditions are given to obtain the switching law that guarantee the practical stability of a class of uncertain SAS whenever persistent bounded sensor faults are present. To reduce the complexity of the nonlinear matrix conditions, we thus provide LMI-based conditions to obtain the observer gains at the cost of increasing the estimation of the ball for which the trajectories of the system are attracted. Next, we estimate the radius of the open ball by solving a minimization problem using the gains obtained by satisfying the LMI conditions. We then propose an algorithm to search for a set of equilibrium points for a class of uncertain SAS with two system modes, which can be applied to DC-DC converters such as boost, buck, buck-boost, and Cuk converters. Finally, experiments using hardware in the loop of a bidirectional buck-boost DC-DC converter illustrate the efficiency of the fault-tolerant strategy.

Given the continuous development of society and the escalating demand for clean energy, there is an imperative focus on wind farm control to overcome the primary obstacle hindering wind farm development: high operation and maintenance costs. This paper presents innovative solutions for intelligent fault-tolerant active power control design based on reinforcement learning, aiming to optimize the balance between grid load and wind farm active power. The proposed solutions effectively handle a range of fault scenarios, addressing both active power control and frequency regulation while safeguarding faulty wind turbines against further deterioration. Through comprehensive simulations conducted on a wind farm benchmark model, the efficacy of these solutions and strategies is demonstrated, showcasing their ability to achieve both passive and active fault-tolerant control across diverse load and fault scenarios.

Motivated by the demand for formation security, this paper studies the fault-tolerant control problem for fixed-wing UAV formation with switching topologies. Based on the application of fixed-wing UAV formation in scenarios involving various severities of faults, this article employs a leader-follower formation control method, focusing on addressing actuator faults, saturation, failures and the consequent communication faults. These challenges are approached as a problem of fault-tolerant control with switching topologies. To tackle these problems, the article delves into the impact of UAV network connections on nodes through neighboring interactions and proposes a novel fault-tolerant formation control method based on graph theory, observer theory, output regulation, and $\mathrm {H}_{\infty} $ robust control theory, which is concluded as an algorithm. The method aims to effectively manage the complexities arising from fault occurrences and ensure the formation cooperative performance. Finally, the proposed control method’s effectiveness is demonstrated in a formation with one leader and four followers through numerical simulations, with a comparative analysis against an alternative method. A formation security schema is developed to provide an application scenario of the proposed method for different kinds of fixed-wing UAV formations.

In this study, a robust $H_\infty$ observer-based PID path tracking control strategy is proposed for Autonomous Ground Vehicle (AGV) to efficiently attenuate the effect of external disturbance, actuator/sensor fault signals, and control saturation to achieve the robust path tracking design. To simplify the design procedure, a novel path reference-based feedforward linearization scheme is proposed to transform nonlinear dynamic AGV system to an equivalent linear tracking error system with nonlinear actuator signal. To protect the AGV system from the corruption of actuator/sensor fault signals, two smoothed signal models are introduced to precisely estimate these fault signals to compensate their corruption. Further, the proposed $H_\infty$ fault-tolerant observer-based PID path tracking control strategy of AGV system can be transformed to an equivalent bilinear matrix inequality (BMI). Consequently, by the proposed two-step method, the complex BMI can be transformed into two linear matrix inequalities (LMIs), which can be easily solved via LMI TOOLBOX in MATLAB. Therefore, control restriction is also considered to meet the constraints of physical actuator saturation on PID controller, making the proposed control scheme more applicable. Finally, the triple-lane change task of AGV is simulated as a numerical example to illustrate the design procedure and to validate the performance of proposed design method.

This article addresses the collaborative wildfire tracking problem in air-ground heterogeneous multiple agent systems (HMAS) with faulty actuators during fire tracking missions. A fault-tolerant dynamic formation control technique is proposed to enable HMAS to dynamically track wildfire propagation using the fractional-order nonsingular terminal sliding mode (FO-NFTSM) control approach. Additionally, a higher-order sliding mode observer (HOSMO) is designed to estimate unavailable linear velocities and multi-source disturbances arising from external disturbances, system uncertainties, and actuator faults. The results demonstrate the effectiveness of the proposed control technique in guiding all agents for accurate wildfire tracking, leading to the convergence of collaborative tracking errors within a finite time. Comparative numerical simulations further validate the efficiency of the proposed approach.

This study proposes a non-fragile fault-tolerant control design based on adaptive robust observer for a type of Markov-type active suspension system with sensor and actuator faults under the external road disturbance. First, the faulty active suspension system is reconfigured into an augmented system by extending the sensor fault as part of the system state vector. Then, an appropriate adaptive observer is designed to estimate the sensor fault, the actuator fault, as well as the active suspension system states simultaneously. Afterwards, an expected non-fragile fault-tolerant control scheme based on state feedback technology is presented to asymptotically stabilize the vertical and pitch motions of the faulty active suspension system. Benefit from its non-fragility, the proposed controller has been proven to be highly stable against its internal gain perturbation. Finally, the comparative simulation results of a half-vehicle active suspension system are provided to validate the effectiveness of the proposed fault-tolerant controller. It is shown that the designed adaptive observer can precisely estimate the sensor and actuator faults, together with the active suspension system states, and the designed non-fragile fault-tolerant control controller can effectively compensate the performance loss of the faulty active suspension system and improve the reliability of the vehicle active suspension system. [ABSTRACT FROM AUTHOR]

The problem of robust, angular velocity-free control and fault-tolerant control for liquid-filled flexible spacecraft attitude maneuver subject to unknown external disturbances is investigated. Moreover, state variables measurement uncertainty and actuator failures are explored. The liquid sloshing effect of spacecraft fuel is equivalent to a two-order spring-mass model, and the flexible attachments are equivalent to the Euler–Bernoulli beams. The external disturbance, multiple failures of the output actuator, and the coupled interference caused by liquid sloshing and vibrations of flexible attachments are distinguished as continuous and percussive disturbances. First, a fault-tolerant control method against the percussive disturbances is proposed, and the robustness performance and characteristics of this method are analyzed. Then, both continuous and percussive disturbances are considered to propose a new fault-tolerant control method based on a nonlinear disturbance observer, which is used to estimate the integrated disturbance. The Lyapunov stability theory proves that the proposed control strategy can make the state variables converge to a small neighborhood of the origin in finite time. Finally, the numerical simulation is used to verify the effectiveness and robustness of the proposed control strategy. [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]

In this article, the problems of fault diagnosis (FD) and fault-tolerant control (FTC) are investigated for a class of nonlinear systems with output measurement noise. Due to the influence of measurement noise in the output sensor, the output observation error cannot be accurately obtained, which causes obstacles to the accuracy of FD. To address this issue, an output filter and disturbance estimator are constructed to decrease the negative effects of measurement noise and observer gain disturbances, and a novel non-fragile neural observer is designed to estimate the unknown states. A new evaluation function is also introduced to detect faults. Then, a novel neural FTC controller is proposed in the presence of faults, to ensure that all the closed-loop system signals are semiglobally uniformly ultimately bounded (SGUUB). The effectiveness of the proposed methodology is verified via numerical simulation of a one-link robot system. [ABSTRACT FROM AUTHOR]