Search Results (775)

In this study, an active fault-tolerant control problem was addressed for a rigid spacecraft in the presence of unknown actuator faults, uncertainties, and disturbances. First, an adaptive sliding mode iterative learning-based observer (ASILO) is proposed for diagnosing and reconstructing unknown faults. It achieves greater accuracy and rapidity while consuming less computing resources by constructing adaptive gain based on an auxiliary error. Specifically, it significantly improved the computational efficiency by 76% compared with the Strong Tracking Kalman Filter while achieving a similar accuracy. It also enhanced the accuracies relative to the traditional ILO and adaptive ILO by 67% and 36%, respectively, and demonstrated 82% and 52% increases in rapidity. Then, fault-tolerant control with resilient prescribed performance (RPP) that can adapt to changing initial conditions and adaptively adjust performance constraints online by sensing faults and error trends is proposed. It avoided the control singularity by constructing adaptive resilient boundaries with almost no impact on the computational overhead. It significantly improved the performance and conservatism. Finally, the robustness and effectiveness of the proposed strategy were demonstrated by numerical simulations. Full article

This paper describes the task of controlling a robot to enable energy savings in the case of one- or two-axis failure. The proposed algorithms are tested in a task similar to “pick and place” but without gripping. Obstacles are present in the robot’s workspace. The goal of the algorithm is to control the robot in such a way that energy consumption is minimized while also avoiding obstacles and ensuring fault tolerance in the event of axis failures. The algorithm uses a developed torque model of the robot, which is employed to calculate the energy requirements for each possible movement step in the robot’s position. In the robot’s control system, artificial intelligence methods are also applied. Specifically, a genetic algorithm is used to generate learning data for the selection of the optimal kinematic configuration of the robot, and a multilayer perceptron is utilized to predict the parameters of the defined reward function. This function is crucial for selecting the optimal action at each time step. The study demonstrates that the application of the algorithm leads to a reduction in robot energy consumption. Studies conducted in simulation and verified on a real robot for 10 different obstacle and target positions and 22 possible kinematic configurations of the robot, consisting of all axes active, any one axis inactive, or any two axes inactive, confirm the energy-saving possibilities. Full article

High-power-range wind generators mainly employ classical variants, with the advantages of low cost, high robustness and acceptable energetic performance, while for low-power applications, the available electrical drive solutions are more numerous. This paper investigates the current trend in this field, indicating simple or complex structures, with or without self-excitation and with or without mechanical or magnetic transmission. The discussed variants are compared in terms of complexity, cost, fault-tolerance capability and estimated energetic performances but also the grid connectivity for standard conditions. The review is completed by testing options and conditions, as well as the methods for parameter determination, which have an important effect on the controllability of the entire system. Full article

The fault-tolerant time-varying formation (TVF) trajectory tracking control problem is investigated in this paper for uncertain multi-agent systems (MASs) with external disturbances subject to time delays under semi-Markov switching topologies. Firstly, based on the characteristics of actuator faults, a failure distribution model is established, which can better describe the occurrence of the failures in practice. Secondly, switching the network topologies is assumed to follow a semi-Markov stochastic process that depends on the sojourn time. Subsequently, a novel distributed state-feedback control protocol with time-varying delays is proposed to ensure that the MASs can maintain a desired formation configuration. To reduce the impact of disturbances imposed on the system, the $H_{\infty }$ performance index is introduced to enhance the robustness of the controller. Furthermore, by constructing an advanced Lyapunov–Krasovskii (LK) functional and utilizing the reciprocally convex combination theory, the TVF control problem can be transformed into an asymptotic stability issue, achieving the purpose of decoupling and reducing conservatism. Furthermore, sufficient conditions for system stability are obtained through linear matrix inequalities (LMIs). Eventually, the availability and superiority of the theoretical results are validated by three simulation examples. Full article

Deep-sea offshore wind power generation has gained increasing attention in the past decade. The low cost and high efficiency of the DC grid system make it more competitive when the transmission distance is over 100 km. As the key enabler of the DC grids, DC converters are necessitated to interconnect the DC lines with different voltage levels. Instead of using auxiliary circuits, this paper proposes a low-cost isolated modular multilevel DC converter (IMMDC) with unipolar-to-bipolar conversion topology to fit into the DC grids with different configurations. Moreover, the proposed DC-current-injection-based fault-tolerant scheme can maintain around 50% power transmission capability even under single pole open-circuit fault conditions for a certain period, enhancing the power supply continuity. The modularity and scalability of the proposed IMMDC topology can fit into different DC grid systems. The effectiveness and feasibility of the proposed topology and control strategy are verified using a 50 kV/±300 kV/100 MW MATLAB/Simulink 2022b simulation model. Full article

Unlike traditional tap changers, which require transformers to be de-energized before making changes, On-Load Tap Changers (OLTCs) can adjust taps while the transformer is in service, ensuring continuous power supply during voltage regulation. OLTCs enhance grid reliability and support load balancing, reducing strain on the network and optimizing power quality. Their importance has grown as the demand for stable voltage and the integration of renewables has increased, making them vital for modern and resilient power systems. While enhanced OLTCs often incorporate stronger materials and improved designs, mechanical components like contacts and diverter switches can still experience wear over time. This can result in longer maintenance intervals. In the era of digitalization, advanced diagnostic techniques capable of detecting early signs of wear or malfunction are essential to enable preventive maintenance for these important components. This contribution introduces a novel method for detecting faults and irregularities in OLTCs, leveraging vibroacoustic signals to enhance OLTC diagnostics. This paper proposes a tolerance-based approach using the envelope of vibroacoustic signals to identify faults. A significant challenge in this field is the limited availability of faulty signal data, which hinders the performance of machine learning algorithms. To address this, this study introduces a nonlinear model utilizing amplitude modulation with a Gaussian carrier to simulate faults by introducing controlled distortions. The dataset used in this study, with data recorded under real operating conditions from 2016 to 2023, is free of anomalies, providing a robust foundation for the analysis. The results demonstrate a marked improvement in the robustness of detecting simulated faults, offering a promising solution for enhancing OLTC diagnostics and preventive maintenance in modern power systems. Full article

This paper presents the design of a fault-tolerant control system based on fault estimation, aimed at enhancing the sustainability and efficiency of a wind energy conversion system using a doubly-fed induction generator. The control architecture comprises a rotor-side converter (RSC) and a grid-side converter (GSC). The RSC is responsible for regulating both active and reactive power, and its model incorporates two linear subsystem representations. A fault-tolerant control (FTC) scheme is developed using a state-feedback controller; this controller is applied to regulate stator and rotor currents. Additionally, for comparison purposes, Proportional–Integral (PI) and Sliding-Mode Controllers (SMCs) are designed to analyze the performance of each controller. Furthermore, a proportional integral observer is employed in the proposed fault-tolerant scheme for actuator fault estimation. Fault detection is achieved by comparing the fault estimation signal with a predefined threshold. The main contribution of this work is the design and validation of a comprehensive active FTC scheme that enhances system reliability and sustainability. It also includes a performance analysis comparing three controllers (PI, SMC, and state-feedback) applied to the RSC. These controllers are evaluated for their ability to regulate active and reactive power in a wind energy conversion system under conditions of non-constant actuator faults. Full article

This paper proposes a novel strategy for the current injection-based control of distributed energy resources connected to weak grids via a voltage source converter. The current injection controller is no longer synchronized with the point of common coupling but with the strong grid point voltage. The strong grid synchronization control strategy improves the output dynamics of the voltage source converter and recovery after faults in weak grids. The phase difference between the voltage source converter and the strong grid voltages caused by the long power lines does not affect the power control. Furthermore, a time delay-compensation method is proposed which tolerates the communication time delay introduced by the transmission of the synchronization signal from the strong grid point. The performance of the proposed control strategy is verified in detail using MATLAB 2023b simulations and real-time digital simulations on a medium voltage model and also validated in an experiment on a low-voltage grid-feeding inverter setup. Full article

This study focuses on helicopter turboshaft engine innovative fault-tolerant fuzzy automatic control system development to enhance safety and efficiency in various flight modes. Unlike traditional systems, the proposed automatic control system incorporates a fuzzy regulator with an adaptive control mechanism, allowing for dynamic fuel flow and blade pitch angle adjustment based on changing conditions. The scientific novelty lies in the helicopter turboshaft engines distinguishing separate models and the fuel metering unit, significantly improving control accuracy and adaptability to current flight conditions. During experimental research on the TV3-117 engine installed on the Mi-8MTV helicopter, a parametric modeling system was developed to simulate engine operation in real time and interact with higher-level systems. Innovation is evident in the creation of the failure model that accounts for dynamic changes and probabilistic characteristics, enabling the prediction of failures and minimizing their impact on the system. The results demonstrate high effectiveness for the proposed model, achieving an accuracy of 99.455%, while minimizing the loss function, confirming its reliability for practical application in dynamic flight conditions. Full article

This study addresses the issue of compromised performance and stability in distributed drive electric vehicles during high-speed operation in the event of motor failure. A fault-tolerant control strategy for distributed drive electric vehicles is proposed, based on model reference adaptive control (MRAC). First, a seven-degree-of-freedom vehicle dynamics reference model is established, from which the output torque of each wheel during stable operation is determined. Secondly, based on the compensation principle that maintains constant longitudinal speed and total torque before and after the fault, the output torque of the remaining wheels is determined to ensure normal vehicle operation in the event of a single motor failure. To improve torque distribution accuracy, an MRAC controller that takes into account the output hysteresis of the motor is designed. The transfer functions of both the reference model and the actual model are derived. Using Lyapunov’s second method, the adaptation rate of the MRAC system is formulated, ensuring that the state of the actual model converges to that of the reference model, thereby achieving adaptive regulation of system parameters and global stability. Finally, simulation experiments are conducted under high-speed dual-lane conditions. The results indicate that, in the case of a single motor failure with constant vehicle speed, the yaw rate and lateral displacement of the vehicle’s center of mass decrease, thereby validating the effectiveness of the proposed fault-tolerant control strategy. Full article

Different types of faults occur in different layers of IoT networks, which affect their fault tolerance. The Controller and Services layers are the weakest links in an IoT network, and any failure in these layers drastically reduces its fault tolerance. The flow of transactions in these layers is also very heavy, requiring load balancing. The major challenge is to retain the maximum Fault tolerance of the IoT network in the presence of failures and varying load conditions in these Layers. This paper proposes a combination of Embedded system networking and a specialised networking of microcontrollers with Service Servers, and a Middleware within the Microcontrollers for load-balancing transactions in different communication paths. These implementations help retain the highest fault tolerance of the IoT network. The fault tolerance of an IoT is improved by 8% due to these proposed implementations. Full article

As maximum power point tracking (MPPT) algorithms have developed towards multi-task intelligent computing, processors in photovoltaic power generation control systems must be capable of achieving a higher performance. However, the challenges posed by the complex environment of photovoltaic fields with regard to processor reliability cannot be overlooked. To address these issues, we proposed a novel approach. Our approach uses error rate and performance as switching metrics and performs joint statistics to achieve efficient adaptive switching. Based on this, our work designed a redundancy-mode switchable three-core processor system to balance performance and reliability. Additionally, by analyzing the relationship between performance and reliability, we proposed optimization methods to improve reliability while ensuring a high performance was maintained. Finally, we designed an error injection method and verified the system’s reliability by analyzing the error rate probability model in different scenarios. The results of the analysis show that compared with the traditional MPPT controller, the redundancy mode switchable multi-core processor system proposed in this paper exhibits a reliability approximately 5.58 times that of a non-fault-tolerant system. Furthermore, leveraging the feature of module switching, the system’s performance has been enhanced by 26% compared to a highly reliable triple modular redundancy systems, significantly improving the system’s reliability while ensuring a good performance is maintained. Full article

This paper presents an online model-free sensor fault-tolerant control scheme capable of tolerating the most common faults affecting an induction motor. This approach involves using neural networks for fault detection to provide the controller with sufficient information to counteract adverse consequences due to sensor faults, such as degradation in performance, reliability, and even failures in the control system. The proposed approach does not consider the knowledge of the nominal model of the system or when the fault may occur. Therefore, a high-order recurrent neural network trained online by the Extended Kalman Filter is used to obtain a mathematical model of the system. The obtained model is used to synthesize a discrete-time sliding mode control. Then, the fault-detection and -isolation stage is performed by independent neural networks, which have as input the signal from the current sensor and the position sensor, respectively. In this way, the neural classifiers continuously monitor the sensors, showing the ability to know the sensor status. The combination of controller and fault detection maintains the operation of the motor during the time of the fault occurrence, whether due to sensor disconnection, degradation, or connection failure. In fact, the MLP neural network achieves an accuracy between 95% and 99% and shows an AUC of 97% to 99%, and this neural network correctly classifies true positives with acceptable performance. The Recall value is high, between 97% and 99%, and the F1 score confirms a good performance. In contrast, the CNN shows a higher accuracy, between 96% and 99% in accuracy and 98% to 99% in AUC. In addition, its Recall and F1 reflect a better balance and capacity to handle complex data, demonstrating its superiority to MLP in fault classification. Therefore, neural networks are a promising approach in areas such as fault-tolerant control. Full article

This paper investigated the tracking problem of mixed ${\mathcal{H}}_{\infty }$ and $L_2-L_{\infty }$ adaptive fault-tolerant control (FTC) for continuous-time interval type-2 fuzzy systems (IT2FSs). For the membership function mismatch and uncertainty between the modules of the nonlinear system, the IT2 fuzzy model is applied to linearly approximate it. The observer can sensitively estimate the system state, and the adaptive fault estimation functions can estimate adaptively the fault signals, which enables the designed adaptive FTC scheme to ensure the asymptotic stability of the closed-loop control system and achieve the desired mixed ${\mathcal{H}}_{\infty }$ and $L_2-L_{\infty }$ tracking performance. The designed adaptive control law can achieve the purpose of dynamic compensation for faults and disturbances, and the introduced lemmas further reduce the design conservatism by adjusting the slack parameters and matrices. Finally, a mass-spring-damping system is used to illustrate the effectiveness of the designed method. Full article

In response to control issues in hypersonic vehicles under external disturbances, model uncertainties, and actuator failures, this paper proposes an adaptive fast smooth second-order sliding mode fault-tolerant control scheme. First, a system separation approach is adopted, dividing the hypersonic vehicle model into fast and slow loops for independent design. This ensures that the airflow angle tracking error and sliding mode variables converge to the vicinity of the origin within a finite time. A fixed-time disturbance observer is then designed to estimate and compensate for the effects of model uncertainties, external disturbances, and actuator failures. The controller parameters are dynamically adjusted through an adaptive term to enhance robustness. Furthermore, first-order differentiation is used to estimate differential terms, effectively avoiding the issue of complexity explosion. Finally, the convergence of the controller within a finite time is rigorously proven using the Lyapunov method, and the perturbation of aerodynamic parameters is tested using the Monte Carlo method. Simulation results under various scenarios show that compared with the terminal sliding mode method, the proposed method outperforms control accuracy and convergence speed. The root mean square errors for the angle of attack, sideslip angle, and roll angle are reduced by 65.11%, 86.71%, and 45.51%, respectively, while the standard deviation is reduced by 81.78%, 86.80%, and 45.51%, demonstrating that the proposed controller has faster convergence, higher control accuracy, and smoother output than the terminal sliding mode controller. Full article