Fault-tolerant Control System Research Articles

Multi-Power System Electrical Source Fault Review

The phrase “Multi-Power System (MPS)” refers to an application that combines different energy conversion technologies to meet a specific energy need. These integrated power systems are rapidly being lauded as essential for future decarbonized grids to achieve optimum efficiency and cost reduction. The fact that MPSs multiply several sources also multiplies their advantages to be environmentally friendly and increases the possibility of energy autonomy as they do not depend on a single source. Consequently, this increases the reliability and reduces the production costs and the size of the storage system. However, the main disadvantages of such a system are the complexity of its architecture and the difficulty in managing the power level, which leads the system to face many faults and sometimes failure. In this case, a fault-tolerant control (FTC) system can automatically adapt to component malfunctions while maintaining closed-loop system stability to achieve acceptable performance. However, on the way to build efficient FTC, one first needs to study the faults that may occur in the system in order to tolerate them. This review paper presents the faults of the MPS electrical sources used in a hybrid system, including a photovoltaic generator and a diesel generator, plus a lead–acid battery as a storage device. Only the most-encountered faults are treated.

Real-Time Simulation and Experimental Implementation of Luenberger Observer-Based Speed Sensor Fault Detection of BLDC Motors

Abstract Modern control systems’ dependability, safety and efficiency have all been improved by studying fault-tolerant control systems (FTCS). FTCS techniques can typically be active or passive controls. The fault detection and diagnosis (FDD) method is used in this study’s active control branch to identify probable faults that could develop in the speed Hall sensors of brushless DC motors (BLDC). FDD methodologies can be categorised into two types, depending on the available data and the process involved: model-based methods and data-based methods. The proposed approach in this study explores the implementation of the Luenberger observer methodology as part of the model-based approach. The chosen methodology was practically implemented and subjected to experimental evaluation. The proposed observer relies on the residual signal, which displays the difference between the plant’s observed and estimated speed signals and serves as a failure alert for the entire system. Given the increasing demand for BLDC motors in various industrial control applications, including medical fields, automation and robotics, this particular motor was selected as a benchmark to thoroughly evaluate and validate the proposed method. The primary contribution of this paper lies in the real-time application of model-based sensor fault detection methods to BLDC motors. The efficiency of the suggested method is showcased through extensive MATLAB simulations, where the obtained results confirm the successful detection of faults with a high level of responsiveness. As a result, the project was successfully implemented in real-time, and the experimental results exhibited a close correlation with the simulated outcomes. This consistency between simulation and practical implementation validates the accuracy and reliability of the proposed methodology for detecting faults in the BLDC motor speed sensor. The results underscore the heightened reliability and safety attained by promptly and accurately detecting sensor faults during the operation of the motor.

An adaptive metaheuristic optimization approach for Tennessee Eastman process for an industrial fault tolerant control system.

The Tennessee Eastman Process (TEP) is widely recognized as a standard reference for assessing the effectiveness of fault detection and false alarm tracking methods in intricate industrial operations. This paper presents a novel methodology that employs the Adaptive Crow Search Algorithm (ACSA) to improve fault identification capabilities and mitigate the occurrence of false alarms in the TEP. The ACSA is an optimization approach that draws inspiration from the observed behavior of crows in their natural environment. This algorithm possesses the capability to adapt its search behavior in response to the changing dynamics of the optimization process. The primary objective of our research is to devise a monitoring strategy that is adaptable in nature, with the aim of efficiently identifying faults within the TEP while simultaneously minimizing the occurrence of false alarms. The ACSA is applied in order to enhance the optimization of monitoring variables, alarm thresholds, and decision criteria selection and configuration. When compared to traditional static approaches, the ACSA-based monitoring strategy is better at finding faults and reducing false alarms because it adapts well to changes in process dynamics and disturbances. In order to assess the efficacy of our suggested methodology, we have conducted comprehensive simulations on the TEP dataset. The findings suggest that the monitoring strategy based on ACSA demonstrates superior fault identification rates while concurrently mitigating the frequency of false alarms. In addition, the flexibility of ACSA allows it to efficiently manage process variations, disturbances, and uncertainties, thereby enhancing its robustness and reliability in practical scenarios. To validate the effectiveness of our proposed approach, extensive simulations were conducted on the TEP dataset. The results indicate that the ACSA-based monitoring strategy achieves higher fault detection rates while simultaneously reducing the occurrence of false alarms. Moreover, the adaptability of ACSA enables it to effectively handle process variations, disturbances, and uncertainties, making it robust and reliable for real-world applications. The contributions of this research extend beyond the TEP, as the adaptive monitoring strategy utilizing ACSA can be applied to other complex industrial processes. The findings of this study provide valuable insights into the development of advanced fault detection and false alarm monitoring techniques, offering significant benefits in terms of process safety, reliability, and operational efficiency.

Fault Tolerant Control of a Dual Star Induction Machine Drive System using Hybrid Fractional Controller

Abstract This paper presents a novel fault tolerant control (FTC) strategy for a dual star induction machine (DSIM) based on the combination of two types of robust controllers, namely a proportional resonant (PR) controller for current regulation and a fractional order PI (FOPI) for speed regulation. This FTC is associated with an indirect rotor indirect rotor field-oriented control (IRFOC) strategy. Fault feedforward compensation of the current components is introduced using the residual signal generated by the calculations passing through the PR controller. The fractional-order PI controller is applied as a feedforward fractional-order perturbation observer to the speed control loop, which attempts to minimise the error induced by the fault. In this context, a fault-tolerant control scheme is achieved. The performance characteristics of the proposed fault tolerant control for a dual star induction machine drive are compared with the fault tolerant control based on the conventional integer order IP (IOPI) to verify the effectiveness of the proposed FTC scheme under various conditions, by examining the robustness of the control in the presence of faults. To evaluate the performance of the proposed technique, simulation results are obtained using the Matlab/Simulink environment. According to the obtained simulation results, the proposed FTC system achieves significantly better responses than the conventional IRFOC system in terms of harmonics in the stator currents, and low oscillations in the electromagnetic torque response.

Assessing the reliability of the hardware and software complex of fault-tolerant control systems

Reliability assessment of hardware and software complexes is relevant due to the expansion of automation and intellectualization for sustainable development of mining and transport systems, energy complexes and mechanical engineering. This paper presents an approach to the reliability assessment of control systems based on functional block diagrams of software and control flows. When assessing the reliability of a control system, software is considered taking into account the close relationship with hardware. The article considers control systems that assume the performance of the required function periodically and repeatedly within the transportation and technological cycle of the system. This is typical for automation of mining and transportation systems, as well as energy complexes. It is taken into account that the main function within the transportation-technological cycle does not require a complex algorithm in contrast to the operating system. Thus, the proposed reliability assessment model allows to take into account the interaction between hardware and software of automated control systems, including the variant of their faulttolerant execution.

Back Propagation Neural Network-Based Fault Diagnosis and Fault Tolerant Control of Distributed Drive Electric Vehicles Based on Sliding Mode Control-Based Direct Yaw Moment Control

Distributed-drive vehicles utilize independent drive motors on the four-wheel hubs. The working conditions of the wheel-hub motors are so harsh that the motors are prone to failing under different driving conditions. This study addresses the impact of drive motor faults on vehicle performance, particularly on slippery roads where sudden faults can lead to accidents. A fault-tolerant control system integrating motor fault diagnosis and a direct yaw moment control (DYC) based fault-tolerant controller are proposed to ensure the stability of the vehicle during various motor faults. Due to the difficulty of identifying the parameters of the popular permanent magnet synchronous wheel hub motors (PMSMs), the system employs a model-free backpropagation neural network (BPNN)-based fault detector. Turn-to-turn short circuits, open-phase faults, and diamagnetic faults are considered in this research. The fault detector is trained offline and utilizes rotor speed and phase currents for online fault detection. The system assigns the torque outputs from both healthy and faulted motors based on fault categories using sliding mode control (SMC)-based DYC. Simulations with four-wheel electric vehicle models demonstrate the accuracy of the fault detector and the effectiveness of the fault-tolerant controller. The proposed system is prospective and has potential for the development of distributed electric vehicles.

Real-time current sensor fault detection and localization in DFIG wind turbine systems

<span lang="EN-US">The degree of reliability is one of the main issues in having the uninterrupted operation of fault-tolerant measurement and control systems. This paper presents a technique for detecting and localizing current sensor fault in doubly fed induction generator wind turbine systems (DFIG-WT). For this purpose, a methodology divided into three steps has been carried out and is presented as follows: the first step is the application of the zero-sequence component as an indicator of the presence of the sensor fault. The second step is based on the application of Concordia transformation on the current signals to generate different criteria for localizing the faulty sensor. Finally, an artificial neural network model is developed to analyze the localization criteria and identify the faulty sensor. All simulations are carried out in MATLAB/Simulink environment. Results show that the proposed technique is effective and can be used in real-time fault detection and localization in the DFIG-WT.</span>

Relaxed Stabilization of Event-Triggered Interval Type-2 T–S Fuzzy Positive Systems With Stochastic Actuator Failure

This paper studies the membership-function-dependent (MFD) stability and positivity analysis of positive Interval Type-2 (IT2) Takagi-Sugeno (T-S) fuzzy fault-tolerant control systems under the event-triggered control framework. Considering the fact that parameter uncertainties may exist in nonlinear plants, the IT2 T-S fuzzy model as an effective mathematical tool is employed to approximately express a complex positive system with nonlinearities and uncertainties. In order to save communication resources and improve the security of system operation, an event-triggered fault-tolerant control strategy is then designed based on the Imperfectly Premise Matching (IPM) concept. In addition, for capturing positivity characteristic and matching with the linear co-positive Lyapunov function, a novel event-triggered condition in the 1-norm rather than in the 2-norm is developed to control the positive IT2 T-S fuzzy systems. Thereinto, the non-convex problem as the first obstacle is required to be cleared since feasible solutions usually cannot be obtained based on the non-convex conditions. For dealing with this problem, a valid approach is presented to conduct convexification with the help of a user-chosen constant vector. Besides, because of event-triggered action, the inherent mismatched issue of the premise variables of the fuzzy model and the ones of the fuzzy controller is encountered. Fortunately, the advanced MFD analysis, which is capable of bringing the information contained in the membership functions (MFs) into the stability and positivity conditions, provides an effective strategy to remove this barrier. Consequently, relaxed results which can accomplish the stability and positivity for wider positive systems are developed and displayed in sum-of-squares (SOS) form. Detailed simulation results are given to clarify the effectiveness of the designed event-triggered fault-tolerant control method.

Active Fault-tolerant Control for Surface Permanent Magnet Synchronous Motor Under Demagnetization Fault

This paper introduces a novel method for controlling a surface permanent magnet synchronous motor (SPMSM) during demagnetization fault conditions. The proposed fault-tolerant control (FTC) system incorporates a combination of a fuzzy extended state observer (FESO) based on an interval type 2 fuzzy logic controller (IT2FLC) and second-order sliding mode control (SOSMC) utilizing the super-twisting algorithm. The FESO aims to identify and eliminate demagnetization faults through reconstruction control. The FTC system enhances the dynamic performance and disturbance rejection of the SPMSM, providing a robust solution in the event of a demagnetization fault.

Adaptive fault tolerant control scheme based on observer-controller integrated design for deep-sea vehicles

This paper investigates the adaptive fault tolerant control problem for deep-sea vehicles subject to thruster saturation, dead zone and measurement noise. In order to reduce the conservativeness of the fault tolerant control system, an integrated design of observer estimation and adaptive fault tolerant control is proposed for deep-sea vehicles based on Nussbaum function. Nussbaum functions are used to separate the compensation of the nonlinear factors such as actuator fault and dead zone in the observer and controller from the traditional generalized uncertainty term. Meanwhile, a diagonal matrix with parameters dynamically adjusted based on the tracking error norm is introduced into the control law. The combined stability analysis of the observer and controller is presented in this paper, and the separation principle of the observer and controller is avoided. Finally, the simulation results of the underwater vehicle verify that the proposed method can effectively reduce the energy consumption of the control signals while maintaining the tracking accuracy, and the largest yaw error under the serious thruster fault is reduced by 40% at least compared with other control schemes.

Promoting the Maneuverability and Fault-Tolerant Control Capabilities of Dual-System/Hybrid VTOL UAVs

This paper presents an active fault-tolerant flight control system (FCS) design for the dual-system/hybrid VTOL UAV configuration that maximally exploits its inherent over-actuation abilities to provide higher levels of fault-tolerance and reliability compared to that of the other VTOL UAVs configurations up to this point. The significance of this study is to convert the main drawback of having the vertical rotors as dead weights during the cruise to a key advantage where all the UAV's controls are kept active during all the flight phases raising the UAV's fault-tolerance and maneuverability abilities. The FCS proposed elaborates on a new architecture for the hierarchical automatic control loops using the dynamic inversion control to design the trajectory tracking virtual commands and employing optimal dynamic control allocation to optimally resolve the controls redundancy. Regarding fault-tolerance, the presented FCS handles simultaneous failures and jamming of all the control surfaces during flight where the control commands to the healthy controls (vertical rotors) were automatically adapted such that the trimming and trajectory tracking were successfully maintained till the mission end. Additionally, this paper addresses for the first time the employment of the controls redundancy to enhance the VTOL UAV's maneuverability in fault-free cases, where we present a study of reducing the minimum turn radius of the UAV when the vertical rotors are employed with the control surfaces during the maneuver. A reduction of 38% in the coordinated steady-level minimum turn radius turn at 35 m/sec flight speed was achieved.

Fault Diagnosis and Condition Monitoring in Wave Energy Converters: A Review

The technology used in wave energy conversion systems is still in the early stages of research and development. There are a number of challenges associated with becoming a commercially viable source of renewable energy due to the high operating and maintenance (O&M) costs. A potential solution for increasing the availability of wave energy converters (WECs) and reducing operating and maintenance costs might involve the implementation of condition monitoring and fault-tolerant control systems, because in some reported WEC systems, 57% of total operational expenses go to maintenance activities. The use of condition monitoring techniques in wind energy systems has, for instance, shown the ability to detect failures months in advance, resulting in savings of 15–20% during the operational phase. This paper reviews the methods proposed (and some used) by researchers to monitor WEC’s condition and diagnose faults. Fault-tolerant control methods developed to improve the reliability of WECs and hence their commercial viability are also reviewed and discussed. In addition, a future research plan is provided here.

Experimental Validation of Current Sensors Fault Detection and Tolerant Control Strategy for Three-Phase Permanent Magnet Synchronous Motor Drives

This paper presents the experimental validation of a Fault-Tolerant Control (FTC) system for Permanent Magnet Synchronous Motor (PMSM) drives, specifically focusing on current sensors. The FTC system is designed to detect and diagnose both single and multiple faults in the current sensors and to reconfigure the control loop to ensure uninterrupted operation in the presence of such faults. Several crucial aspects are addressed in the proposed approach, including fault detection, isolation of faulty sensors, and reconfiguration of the control system through accurate current estimation. To achieve this, a novel adaptation of the Luenberger observer is proposed and employed for estimating the stator currents. The effectiveness of the fault-tolerant control strategy is demonstrated through experimental tests conducted on a 7.2 kW PMSM utilizing a field-oriented vectorial strategy implemented in a dSpace 1104 platform.

Flight Test Techniques - An Overview

Flight test techniques towards performance, handling qualities, stability and control are brought out. Hazardous flight conditions are covered. Instrumentation aspects are highlighted. Data reduction and some flight evaluation cases are addressed. Airframe structural performance under different conditions of flight test are explained. Fault tolerant flight control system testing is brought out. Features related to online development of restructurable and reconfigurable control laws are highlighted.

Fault-tolerant control strategy aiming at motion capability optimization for space manipulator with joint effectiveness partial loss failure

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%.

Fault-tolerant control strategy of six-phase permanent magnet synchronous motor based on deadbeat current prediction.

The fault-tolerant control after phase loss is crucial in the studies of the six-phase permanent magnet synchronous motor (PMSM), and the one phase loss is the most frequent phase loss fault. To improve the system instability caused by nonlinear and time-varying perturbations of inductance parameters in a double-Y phase shifted 30° six-phase PMSM, an improved deadbeat predictive current fault-tolerant control (DPC-FTC) method is proposed in this study. The transformation matrix after single-phase open-phase is first reduced and reconstructed, and the reduced-dimensional voltage equation is derived. Based on this equation, the deadbeat current predictive control is then used to predict the expected voltage using the current feedback value and the reference value, so as to shorten the response time and improve the overall control effect. The voltage equation after parameter perturbation is rewritten, and the current discrete transfer function under constant expected voltage before and after parameter perturbation is calculated. Afterwards, to further improve the low stability of fault-tolerant control after phase loss, which is caused by the inductance parameter perturbation of the control system, the weight coefficient is introduced in order to enhance the deadbeat predictive current control so that it splits and optimizes the direct-quadrature axis current. The stability of the system is then analyzed. By changing the weight coefficient, the fault-tolerant control system has a wider stable working range. Finally, the simulation model and experimental platform are completed. The results show that the improved DPC-FTC method improves the permissible inductor parameter uptake range by a factor of 1/β, reduces the current static difference by 32.05% and 46.02% when the inductor parameter is mismatched by a factor of 2, reduces the current oscillation and effectively reduces the sensitivity of system stability to inductor parameter uptake.

Wind Turbine Active Fault Tolerant Control Based on Backstepping Active Disturbance Rejection Control and a Neurofuzzy Detector

Wind energy conversion systems have become an important part of renewable energy history due to their accessibility and cost-effectiveness. Offshore wind farms are seen as the future of wind energy, but they can be very expensive to maintain if faults occur. To achieve a reliable and consistent performance, modern wind turbines require advanced fault detection and diagnosis methods. The current research introduces a proposed active fault-tolerant control (AFTC) system that uses backstepping active disturbance rejection theory (BADRC) and an adaptive neurofuzzy system (ANFIS) detector in combination with principal component analysis (PCA) to compensate for system disturbances and maintain performance even when a generator actuator fault occurs. The simulation outcomes demonstrate that the suggested method successfully addresses the actuator generator torque failure problem by isolating the faulty actuator, providing a reliable and robust solution to prevent further damage. The neurofuzzy detector demonstrates outstanding performance in detecting false data in torque, achieving a precision of 90.20% for real data and 100% for false data. With a recall of 100%, no false negatives were observed. The overall accuracy of 95.10% highlights the detector’s ability to reliably classify data as true or false. These findings underscore the robustness of the detector in detecting false data, ensuring the accuracy and reliability of the application presented. Overall, the study concludes that BADRC and ANFIS detection and isolation can improve the reliability of offshore wind farms and address the issue of actuator generator torque failure.

Novel barrier Lyapunov function-based backstepping fault tolerant control system for an ROV with thruster constraints

This paper proposes a novel Barrier Lyapunov Function (BLF)-based fault tolerant control system for a work-class Remotely-Operated Vehicle (ROV) with thruster saturation and rate limits. The proposed control system is composed of a fixed-time state and fault observer, and a novel BLF-based backstepping controller. The novel BLF-based controller is introduced to eliminate the sensitivity of conventional BLF-based algorithms to rate limitations of the thrusters. In addition, it helps to improve the performance during initial transients and provides faster response to fault and failures. The observer combines the dynamics of the ROV with the dynamics of the thruster system. This combination facilitates estimation of thruster faults and failures independently of the system uncertainties, so highly improved performance is expected compared to conventional extended-state observers. To account for thruster constraints and provide fault tolerance, control allocation is utilized. Stability analysis is performed for the observer and controller, and it is shown that the state estimation error is fixed-time convergent, the fault estimation unit is bounded-input bounded-output stable, and the controller is exponentially stable. Simulations are carried out and comparisons are made with several asymptotic and finite-time BLF-based control systems. The simulations confirm the superior performance of the proposed control system.

Saturation-tolerant prescribed function-based adaptive fault-tolerant tracking control for ACV with uncertainty and faults

This paper presents an adaptive fault-tolerant control system for Trajectory tracking of air-cushioned vehicles (ACVs) in the presence of actuator saturation, internal and external system perturbations. The proposed approach utilizes a state observer to estimate and compensate for the disturbances and saturation fault information present in the tracking system of the ACV. To achieve the desired performance, a new prescribed performance function (PPF) is introduced that is both global and saturated-tolerant. The PPF ensures that the error converges to a predetermined function in fixed time for any initial value, although it weakens the limits on the amount of overshoot. The combination of the observer and PPF with a backstepping fault-tolerant controller leads to improved transient and steady-state performance of the tracking control system for ACV. Simulation results demonstrate the effectiveness of the proposed approach.

Fault-tolerant control based on reinforcement learning and sliding event-triggered mechanism for a class of unknown discrete-time systems

This paper presents an adaptive fault-tolerant control (FTC) system based on reinforcement learning using an even-triggered mechanism. The even-triggered mechanism is established through a justifiable sliding surface and triggered function, without the need for any fault detection or observer. The learning laws are derived to ensure the convergence of internal signals and tracking error, and an actor–critic architecture is designed accordingly. To validate the proposed scheme, an experimental system is constructed and tested using five typical actuator faults. The results indicate a positive closed-loop performance and a reduction of approximately 25% in data transmission for both cases with and without faults.