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The adaptive neural tracking fault‐tolerant control problem is considered for nonlinear non‐affine switched systems with sensor faults via dynamic surface control (DSC) technique under arbitrary switchings within predefined‐time interval. During the controller design process, the non‐affine formation strictly processed so that the implicit control signals can be transformed into explicit ones. The introduction of hyperbolic tangent function to design the control signal eliminates the singularity at the same time, but also avoids the tedious discussion of the segmentation function to solve the singularity. Considering Lyapunov stability theorem, an adaptive fault tolerant control approach is presented, which means that the settling‐time can be programmed by the user practical specification under arbitrary switching, the predefined time boundedness of all closed‐loop signals can be ensured, and the influence of sensor faults can be compensated. The effectiveness of the presented method is verified via simulation results. [ABSTRACT FROM AUTHOR]

The advancement of renewable energy technology has been significantly aided by solar photovoltaics (PV). Since solar PV is a weather‐dependent source, it cannot be dispatched. To ensure that the solar PV system can harvest the maximum amount of electricity for the available irradiance level, maximum power point tracking (MPPT) algorithms are used. For standalone low‐voltage DC (LVDC) microgrids to utilize the energy storage system as efficiently as possible, maximum power extraction is essential. The sensed PV voltage and current are essential for these MPPT algorithms to ensure that the maximum power point of the panel is captured. This work proposes an effective fault‐tolerant control (FTC) scheme for the solar PV subsystem in the LVDC microgrid that can seamlessly extract the maximum power despite the PV voltage sensor being faulty. The proposed FTC scheme uses a sliding mode observer (SMO)‐based method to detect and isolate PV voltage sensor faults in the standalone LVDC microgrid. The efficacy of the proposed FTC is assessed in a range of circumstances involving load disturbance, irradiance change, and various sensor fault scenarios. The performance of the proposed FTC is validated using experimental analysis on the LVDC microgrid testbed and MATLAB simulations. Given a faulty PV voltage sensor, at a given operating condition of the microgrid, the proposed FTC scheme is successful in reducing the additional power burden on the battery storage by at least two times. Consequently, the additional discharge in terms of SoC is also seen to be decreased by at least 9%. The proposed FTC technique outperforms the popular MPPT approaches for solar PV in terms of PV voltage sensor fault tolerance in the microgrid. [ABSTRACT FROM AUTHOR]

In this paper, a finite‐time functional observer design for linear time‐invariant descriptor systems is presented. A generalized observer based on additional degrees of freedom is developed. This observer can be considered of a general structure since the existing proportional observer (PO) and proportional‐integral observer (PIO) constitute its particular cases. The main contribution is to generate a finite‐time convergent functional observer for descriptor systems by combining two generalized functional observers. The obtained results are applied to the simultaneous state, sensor, and actuator faults estimation. A numerical example is given to illustrate the approach. The obtained results show that the observer is robust in presence of uncertainties, compared to its particular cases. [ABSTRACT FROM AUTHOR]

This study focuses on examining the issues of extended dissipative-based composite anti-disturbance reliable tracking control for stochastic Takagi–Sugeno fuzzy systems afflicted by sensor faults and multiple disturbances. Explicitly, the multiple disturbances include the autonomous Wiener process and non random noises with partly known information that are generated by an exogenous plant. To address the difficulty of estimating the non random disturbance while considering the partially known information, the study incorporates a fuzzy stochastic disturbance observer. Furthermore, to solve the output tracking issue of the system under consideration, a modified repetitive tracking control is implemented. Then, combining the methodologies of disturbance observer-based control technique with modified repetitive control, a composite anti-disturbance reliable tracking control strategy is developed for achieving the intended tracking goal despite the multiple disturbances and sensor faults encountered. Additionally, the proposed approach involves extended dissipativity scheme, which includes $ H_\infty $ H∞, passivity, dissipativity, and $ \mathfrak {L}_2 - \mathfrak {L}_\infty $ L2−L∞ performances in a unified framework. In particular, by employing Lyapunov–Krasovskii functional approach, the essential stability criteria for the proposed closed-loop system are articulated in the form of linear matrix inequalities. Additionally, numerical illustrations are presented to exemplify the application and efficiency of the developed control protocol. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, the problem of finite‐horizon state estimation is studied for a class of time‐varying complex networks with sensor faults. The phenomenon of measurement quantization is considered such that the measurements are quantized probabilistically before transmitted to the state estimator. To deal with the unknown sensor fault, a neural network is introduced to appropriate the sensor fault whose weights are updated based on estimation error and the gradient descent method. Our aim is to design state estimators so that the state estimation errors are finite‐time bounded. First, sufficient conditions are established to ensure the existence of the desired state estimators. Then, the gains of the state estimators are derived in terms of the solutions to a set of recursive matrix inequalities. Finally, the usefulness of our estimation approach is confirmed by an illustrative example. [ABSTRACT FROM AUTHOR]

This review paper offers a comprehensive examination of the various types of faults that occur in inverters and the methods used for their identification. The introductory segment investigates the internal component failures of voltage-source inverters (VSIs), examining their failure rates and the consequent effects on the overall system performance. Subsequently, this paper classifies and clarifies the potential malfunctions in components and sensors, placing particular emphasis on their frequency of occurrence and the severity of their impact. The examination encompasses issues associated with transistors, including open circuits, short circuits, gate firing anomalies, as well as failures in capacitors, diodes, and sensors. Following this, the paper delivers a comparative assessment of fault diagnosis techniques pertinent to each type of component, appraised against specific criteria. The concluding section encapsulates the findings for each fault category, delineates the fault detection and diagnosis (FDD) methodologies, analyzes the outcomes, and provides recommendations for future scholarly investigation. [ABSTRACT FROM AUTHOR]

The paper deals with the estimation of sensor faults for dynamic systems as well as the assessment of the uncertainty of the resulting estimates. For that purpose, it is assumed that the external disturbances are bounded within an ellipsoidal domain. This allows considering both stochastic and deterministic process and measurement uncertainties. Under such an assumption, a fault diagnosis scheme is developed with a prescribed convergence rate and accuracy. To achieve fault estimation, a conversion into an equivalent descriptor system is utilized. The paper provides a full stability and convergence analysis of the estimator including observability analysis. As a result, a set of complementary fault uncertainty intervals is obtained, which are minimized in such a way as to obtain a minimum detectable sensor fault. The final part of the paper exhibits a numerical example concerning fault estimation of a multi-tank system. The obtained results clearly confirm the performance of the proposed estimator expressed in the minimum detectable fault intervals.

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]

This article studies the fault‐tolerant consensus issue of a class of nonlinear two‐time‐scale‐agent systems with heterogeneous sensor faults and external disturbances. First, the decentralized proportional integral (PI) observer for each agent is constructed to estimate the states and sensor faults simultaneously. Then, a distributed adaptive protocol based on the decentralized PI observer is designed to guarantee the consensus of the underlying systems. Some sufficient existence conditions are given in terms of linear matrix inequalities (LMIs) for the decentralized PI observer and the fault tolerant consensus protocol. Note that H∞$$ {H}_{\infty } $$ performance index is employed to attenuate the influence of sensor faults and disturbances on state estimation and the consensus of the underlying systems. Finally, the effectiveness and superiority of the proposed method are demonstrated by an application example and a numerical example. [ABSTRACT FROM AUTHOR]

Sensors are a key component in industrial automation systems. A fault or malfunction in sensors may degrade control system performance. An engineering system model is usually disturbed by input uncertainties, which brings a challenge for monitoring, diagnosis, and control. In this study, a novel estimation technique, called adaptive unknown-input observer, is proposed to simultaneously reconstruct sensor faults as well as system states. Specifically, the unknown input observer is used to decouple partial disturbances, the un-decoupled disturbances are attenuated by the optimization using linear matrix inequalities, and the adaptive technique is explored to track sensor faults. As a result, a robust reconstruction of the sensor fault as well as system states is then achieved. Furthermore, the proposed robustly adaptive fault reconstruction technique is extended to Lipschitz nonlinear systems subjected to sensor faults and unknown input uncertainties. Finally, the effectiveness of the algorithms is demonstrated using an aircraft system model and robotic arm and comparison studies. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to study a neural network-based fault-tolerant controller to improve the tracking control performance of an unmanned autonomous helicopter with system uncertainty, external disturbances and sensor faults, using the prescribed performance method. Design/methodology/approach: To ensure that the tracking error satisfies the prescribed performance, the authors adopt an error transformation function method. A control scheme based on the neural network and high-order disturbance observer is designed to guarantee the boundedness of the closed-loop system. A simulation is performed to prove the validity of the control scheme. Findings: The developed adaptive fault-tolerant control method makes the system with sensor fault realize tracking control. The error transformation function method can effectively handle the prescribed performance requirements. Sensor fault can be regarded as a type of system uncertainty. The uncertainty can be approximated accurately using neural networks. A high-order disturbance observer can effectively suppress compound disturbances. Originality/value: The tracking performance requirements of unmanned autonomous helicopter system are considered in the design of sensor fault-tolerant control. The inequality constraint that the output tracking error must satisfy is transformed into an unconstrained problem by introducing an error transformation function. The fault state of the velocity sensor is considered as the system uncertainty, and a neural network is used to approach the total uncertainty. Neural network estimation errors and external disturbances are treated as compound disturbances, and a high-order disturbance observer is constructed to compensate for them. [ABSTRACT FROM AUTHOR]

This paper provided a robust strategy for controlling nonlinear interconnected large-scale systems subject to a sensor fault. The inherent challenge in controlling such systems is to sustain robust closed-loop stability and performance in nonlinear interactions, faults, and exogenous inputs. A new closed-loop structure has been devised to tackle this challenge by integrating the sliding mode controller with the unknown input observer. By exploiting the decoupling capability of the controller and observer, this integration ensures the robustness of the closed-loop system against the simultaneous effect of interaction, fault, and disturbance. In this context, the unknown input observer has been constructed to supply the controller with an accurate sensor fault assessment despite additional unknown inputs. A novel approach is proposed for designing a decentralized fault-tolerant control system that utilizes sliding mode control and proportional-integral-derivative controller tuning via a bacterial foraging optimization algorithm to compensate for the fault effect, leading to a robust output performance. The observer and controller gains are accomplished by utilizing the H∞ performance and linear matrix inequality formulation. The Lyapunov technique is used to demonstrate stability. A power system model emphasizes the proposed approach's robustness and effectiveness. The system performance with and without the proposed controller was compared, where the simulation results show a fast reaction to offset undesirable impacts, whereas the state estimation error approaches to zero in each subsystem. [ABSTRACT FROM AUTHOR]

Sensor faults are a common type of failure in heat pump systems, which can seriously affect the normal operation of systems. Self-correction of the sensor fault in the system is crucial. State-of-the-art sensor fault correction methods based on data-driven and physical models face challenges, such as the need for co-located sensors, accurate physical models, and a large amount of labeled data, greatly limiting their applicability. This paper proposes using machine learning methods for fault self-correction. Firstly, a data self-correction strategy based on the convolutional autoencoder is introduced. Furthermore, an artificial sample generation strategy is proposed to address the scarcity of sensor fault data for data-driven training of the self-correction model. The results demonstrate that the proposed method effectively self-corrects both single and multiple faults. Simultaneously, thermal fault diagnosis evaluations reveal over 90 % accuracy in corrected data, with a maximum diagnostic improvement of 53.5 %. Furthermore, the study shows that the number of parameters is crucial for effective correction, underscoring that over-constraint is essential for successful self-correction.

Batteries are prone to faults that may arise because of vibrations, deformations, collisions, or improper usage. These faults can be sorted into two main categories: internal and external faults. In this study, external battery faults, particularly sensor faults that affect the measurement of current and voltage, are investigated. This paper proposes a fault-detection strategy that is built on different variants of the Smooth Variable Structure Filter (SVSF) for the detection of such faults in a battery cell. SVSF is applied to estimate the State of Charge (SoC) and terminal voltage of the battery. A modified decision signal is calculated using the residual signals of the filters to detect faults using the Cumulative Sum (CUSUM) strategy. The performance of the SVSF is compared with that of the Extended Kalman Filter (EKF). The effectiveness of the proposed method is demonstrated for detecting and isolating different external faults in a wide range of fault scenarios. The proposed SVSF-based methods not only improve fault-detection accuracy but also significantly decrease the fault-detection time in some scenarios compared to EKF, which is a critical factor for safety.

This paper presents an approach for design a continuous-time interval observer for linear parameter varying (LPV) systems in the presence of disturbances that are considered unknown but bounded. The proposed observer is used for the design of a sensor fault detection scheme using the input to state stability (ISS) approach through a Lyapunov quadratic function. The conditions of stability and positivity are presented by a set of linear matrix inequalities (LMIs). The performance of the proposed method is shown in simulation using a case study based on a single-link flexible joint robotic system.

This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated. [ABSTRACT FROM AUTHOR]

Summary: This research presents the design of a fault‐tolerant system based on a dual extended Kalman filter (DEKF) and a bank of two high‐gain observers to ensure the continuous operation of an internal combustion engine (ICE) despite total and partial faults in the mass airflow (MAF) sensor, temperature sensor, and pressure sensor. The DEKF aims to estimate the air mass flow rate (m˙th$$ {\dot{m}}_{th} $$) through the throttle valve. The DEKF uses the temperature and pressure measured in the intake manifold to accurately estimate the (m˙th$$ {\dot{m}}_{th} $$), a failure in any of both sensors will not allow the correct functioning of the DEKF. Therefore, a bank of two high‐gain observers is proposed to estimate the pressure and temperature from only one measure. Observer 1 estimates the temperature and pressure from the pressure, and Observer 2 uses the temperature for estimating pressure and temperature. Hence, if the temperature or pressure sensors fail, the estimated temperature or pressure will replace the ill‐measure signal. The results show the effectiveness of the proposed fault‐tolerant system in the case of one sensor or multiple sensor faults. [ABSTRACT FROM AUTHOR]

Supply chilled water temperature (SCWT) is an important variable for the efficient and stable operation of heating, ventilation, and air conditioning (HVAC) systems. A precisely measured value ensured by the continuous reliability of the temperature sensor is essential for optimal control of an HVAC system because temperature sensor faults can affect the chiller operation and waste energy. Therefore, temperature sensor fault-detection strategies are imperative for maintaining a comfortable indoor thermal environment and ensuring the efficient and stable operation of HVAC systems. This study proposes a fault-detection method for an SCWT sensor using a virtual sensor based on a long short-term memory-autoencoder. The fault-detection performance is evaluated considering a case study under various sensor fault scenarios to evaluate changes in indoor thermal comfort and energy consumption after correcting sensor faults detected by the virtual sensor. The results verify excellent fault-detection performance in various fault scenarios (F-1 scores ranging from 0.9350 to 1.000). After correcting the SCWT fault, indoor thermal comfort is steadily maintained without additional energy consumption (indoor set-point temperature unmet hour reduced by a maximum of 105.7 hours, and energy consumption decreased by up to 1.8%). [ABSTRACT FROM AUTHOR]

The design of an accurate observer is still a challenging problem for nonlinear systems subject to fault signals. This paper addresses the problem of simultaneous state and sensor fault estimation for discrete-time nonlinear systems. An uncoupled multimodel approach is adopted to deal with nonlinear systems subject to sensor faults. A simple sensor fault dynamic is exploited to transform this fault signal into a pseudo-actuator fault scenario. A new sliding mode multiobserver is proposed for the estimation of sensor faults and system states. This multiobserver is computed as a fusion of a set of local sliding mode observers with respective activation functions. The asymptotic stability of the proposed multiobserver is formulated in terms of linear matrix inequalities using the Lyapunov theory. A simulation example is retained to demonstrate the effectiveness of the proposed strategy. As well, a real-time application on a transesterification reactor is carried out to validate the theoretical results. [ABSTRACT FROM AUTHOR]

Sensors are part of the medical, industrial, automotive, and other appliances and contribute to the overall safety of the whole system. Safety standards for different sectors are imposed, and safety should be addressed already on the sensor design level. There are multiple possible approaches to the sensor fault detection, each with its own advantages and disadvantages. The paper reviews the literature about the main sensor fault types as well as analytical and hardware redundancy and approaches to the fault diagnosis and identification. More specifically, sensor bias, drift, gain, noise, hardware failures, random faults, and complete failures are listed. For the analytical redundancy, the paper describes the parity space approach (PSA), principal component analysis (PCA), independent component analysis (ICA), factor analysis, and minimum mean square error (MMSE) method. The steps of each method and its application are shortly explained. Virtual sensors and decision by majority are proposed as possible alternatives to the hardware redundancy. Choosing the most appropriate method for a particular use enhances the performance without changing the hardware. An in-depth comparison of the methods and its result is believed to optimise the sensor selection. [ABSTRACT FROM AUTHOR]

The filling pressure control is one of the key technologies of wet clutches. Pressure sensor is a key component to realize the closed-loop control of the filling pressure. However, with the working hours increasing, the pressure sensor is prone to be faulty. To ensure the filling performance of wet clutches, this article investigates a pressure sensor fault diagnosis and fault-tolerant control strategy for the filling phase of wet clutches. First, the dynamic model of a hydraulic clutch control system is established where the clutch pressure sensor fault is considered. Second, the optimal filling pressure reference trajectory is constructed by Pontryagin's minimum principle. In the fault-free case, a feedback controller is designed to track the desire pressure trajectory with an observer estimating the unmeasurable state of the system. Third, an adaptive observer is constructed to estimate the pressure sensor fault. Based on the estimated sensor fault information, a pressure sensor fault-tolerant control strategy is proposed for the filling phase of wet clutches. Simulation results show that the proposed fault-tolerant controller can realize smooth and precise clutch-filling control with pressure sensor fault. [ABSTRACT FROM AUTHOR]

Fault occurrence in practical systems, if not addressed, can cause diminished performance or even system breakdown. Therefore, fault detection has emerged as a crucial challenge in ensuring system safety and reliability. This paper presents a novel fuzzy observer aimed at reconstructing actuator and sensor faults in nonlinear systems, even when subjected to external disturbances. The approach we propose utilizes the Takagi-Sugeno fuzzy model and Lyapunov function. Initially, by filtering the system output, we construct a system where actuator faults correspond to the original actuator and sensor faults. Subsequently, the impact of disturbance on state estimations is minimized by employing the H-infinity performance criteria. We demonstrate that, for non-disturbed systems, these estimations gradually converge to their true values. In designing the observer gains, transformation matrices are derived by solving linear matrix inequalities. Our approach boasts some advantages over existing methods. By assuming that the premise variables are immeasurable, we enhance the usability of our approach. As a proof of concept, we evaluate two practical systems. The simulation results underline the benefits of our proposed method in terms of rapid and accurate fault detection performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Refrigeration Technology is the property of Shanghai Society of Refrigeration and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

In this article, for a class of stochastic pure-feedback nonlinear systems with simultaneous actuator and sensor faults, the problem of adaptive fault-tolerant control is examined. The stochastic pure-feedback nonlinear system is first converted into a strict-feedback by applying the mean value theorem and radial basis function neural networks are used to approximate the unknown functions. Only one adaptive parameter needs to be calculated online rather than the actual weight vector elements by determining the greatest value of the norm of the neural network weight vector. With the help of regrouping and parameter separation methods, the unavailability of state variables caused by sensor faults is addressed. The Lyapunov function methods and the backstepping recursive design technique are used to design an adaptive fault-tolerant controller. It is shown that by choosing proper the design parameters, the tracking errors converge to a small region of the origin, and all the signals in the closed-loop system are bounded in probability. The performance of the proposed controller is illustrated using a numerical example and a real-world example of a rigid robot manipulator system. [ABSTRACT FROM AUTHOR]

In recent years, electric vehicles powered by lithium-ion batteries have developed rapidly, and the safety and reliability of lithium-ion batteries have been a paramount issue. Battery management systems are highly dependent on sensor measurements to ensure the proper functioning of lithium-ion batteries. Therefore, it is imperative to develop a suitable fault diagnosis scheme for battery sensors, to realize a diagnosis at an early stage. The main objective of this paper is to establish validated electrical and thermal models for batteries, and address a model-based fault diagnosis scheme for battery sensors. Descriptor proportional and derivate observer systems are applied for sensor diagnosis, based on electrical and thermal models of lithium-ion batteries, which can realize the real-time estimation of voltage sensor fault, current sensor fault, and temperature sensor fault. To verify the estimation effect of the proposed scheme, various types of faults are utilized for simulation experiments. Battery experimental data are used for battery modeling and observer-based fault diagnosis in battery sensors. [ABSTRACT FROM AUTHOR]

Semi Batch Reactors (SBR) are mostly used to carry out exothermic reactions (synthesis of 4-Chloro-3-Nitrobenzene-tri-fluoride) where a huge amount of heat is evolved. The excess enthalpy generated is removed by the cooling system which encloses the reactor. SBRs are subjected to several uncertainties, external and internal disturbances, and various other faults that occur during the processing of reactants. In the absence of a cooling system, the product starts to decompose yielding gaseous components which raise reactor temperature and pressure, finally leading to thermal runaway. From model-based effective prognosis, it was found that the reaction is very much sensitive to parameters like Damkoler number, activation energy, and the reaction-enthalpy-coefficient. In order to avoid hazardous situations, a suitable controller must be designed. In this work, a Fault Tolerant Sliding Mode Controller is found to be a promising tool to confront parametric uncertainties, disturbances, and faults. A Fault Tolerant Sliding Mode Observer is designed to observe the unmeasured disturbance and parameters. Nitration of 4-Chlorobenzotrifluoride exhibits both interphase and intraphase mass-transfer phenomenon. The proposed control strategy was demonstrated on the SBR process and compared with the conventional control scheme to check feasibility and effectiveness like cooling rate, computation time, root mean square error, and mean square deviation. To guarantee convergence of the newly developed estimator and stability of the closed-loop controller, Lyapunov criteria are well investigated. [Display omitted] • Nonlinear disturbance observer with event-trigger cubature Kalman filter for fault-tolerant control of batch reactor. • The observer helps in suppressing reactor temperature, and pressure deviations to avoid thermal runaway. • Desired trade-off between transmission rate and state/parameter-based prediction achieved by tuning threshold parameter. • An approach to yield a tractable approximation to FTSMC problem by incorporating ETCKF/CKF observer schemes is proposed. • The controller takes care of structured/unstructured plant-model mismatch and noisy process measurement. • Field-oriented control of SBR for nitration of 4-Chlorobenzotrifluoride with and without actuator failure/sensor fault. • Convergence analysis and the robust stability of the closed-loop system are proved via the Lyapunov theorem. [ABSTRACT FROM AUTHOR]

Sensors are a key component in industrial automation systems. A fault or malfunction in sensors may degrade control system performance. An engineering system model is usually disturbed by input uncertainties, which brings a challenge for monitoring, diagnosis, and control. In this study, a novel estimation technique, called adaptive unknown-input observer, is proposed to simultaneously reconstruct sensor faults as well as system states. Specifically, the unknown input observer is used to decouple partial disturbances, the un-decoupled disturbances are attenuated by the optimization using linear matrix inequalities, and the adaptive technique is explored to track sensor faults. As a result, a robust reconstruction of the sensor fault as well as system states is then achieved. Furthermore, the proposed robustly adaptive fault reconstruction technique is extended to Lipschitz nonlinear systems subjected to sensor faults and unknown input uncertainties. Finally, the effectiveness of the algorithms is demonstrated using an aircraft system model and robotic arm and comparison studies.

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The faults of actuators and sensors can lead to abnormal operations or even system faults in multi-agent systems (MASs). To address this issue, this paper proposes an adaptive fault-tolerant control (FTC) algorithm for leaderless/leader–follower MASs against actuator and sensor faults. First, extended states integrating the fault components are constructed and the MAS is transformed into a descriptor system form. Then, a sliding-mode observer is designed for the transformed MAS. Based on the estimated MAS states and faults, adaptive FTC algorithms are developed, which update the control gains with the distributed tracking error. Finally, numerical simulations demonstrate that the proposed method can guarantee MAS stability against actuator and sensor faults. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical model based on the T-S fuzzy model is proposed to solve the fault estimation (FE) and fault-tolerant control (FTC) problem for singular nonlinear time-varying delay (TVD) systems with sensor fault. TVD is is extremely difficult to solve and the Laplace transform is devised to build an equal system free of TVD. Additionally, the sensor fault is changed to actuator fault by the developed coordinate transformation. A fuzzy learning fault estimator is first built to estimate the detailed sensor fault information. Then, a PI FTC scheme is suggested aiming at minimizing the damage caused by the fault. Simulation results from multiple faults reveal that the FE and FTC algorithms are able to estimate the fault and guarantee the system performance properly. [ABSTRACT FROM AUTHOR]

The electro-hydraulic servo system (EHSS) usually faces multiple sensor faults and disturbances, which is difficult to achieve good tracking control, reliability, and stability control. In this article, an advanced fault-tolerant controller is proposed for an EHSS to deal with the above challenge. The three fault observers, called nonlinear unknown input observers (NUIOs), are developed to effectively estimate the position, velocity, pressure sensor faults and the system states. The fault detection, estimation, and isolation are then presented as effective for multiple sensors failure at a time. The first NUIO for position sensor fault is utilized for the tracking control, while the other NUIOs are used for alarm proposal. The adverse effects caused by the matched and unmatched disturbances are eliminated by two extended state observers (ESOs). In addition, to avoid the "explosion of complexity" when computing the derivatives of virtual control laws, the dynamic surface control is applied to design the fault-tolerant control (FTC) scheme. The Lyapunov principle ensures system stability under lumped disturbance and faulty conditions. Finally, simulation studies and evaluation results are performed to demonstrate the validity of the proposed FTC algorithm. [ABSTRACT FROM AUTHOR]

This paper proposed neural networks to continuously provide alternative constructed signals for vehicle and wheel speed sensors utilized for the Anti-Lock Braking System (ABS), which serves as the fault tolerant control method. These alternative constructed signals are used for two purposes. The first is to generate residual signals, and the second is to be adopted instead of isolated faulty signals. The residual signal is generated by extracting the difference between the alternative constructed signals and the corresponding actual signals. These residual signals serve as an indication of fault occurrence and to express that fault severity. Whenever a fault occurrence is detected and diagnosed in one of the sensor’s signals, the faulty signal is isolated and replaced by the corresponding constructed signal to maintain the system's normal behavior under a faulty condition. The range of data covered under the proposed estimating neural networks is huge, continuous in time, and not sampled. In this work, the range of the data lies between [50 to 120 km/h] when the braking is started. That cannot be performed by any available method. These models' training process is based on the Levenberg-Marquardt (LM) algorithm, implemented and tested by MATLAB/Simulink. The results show that these models can accurately map the measured data into the desired output through the best-fit functions. The fast response of the trained models makes them suitable for real-time alternative signals for fault-tolerant purposes for speed sensors during hard or panic braking.

The fault-tolerant control system (FTCS) for a permanent magnet synchronous motor (PMSM) is presented and an analysis of speed sensor faults is proposed in this paper. The detection and compensation of speed sensor faults is based on the simple comparison of the measured signals with the signals estimated by reduced-order sliding mode observer (SMO). The focus of the work is on the presentation of simulation and experimental research for the most common speed sensor failure types: complete signal loss, gain error, and measurement noise. Additionally, an analysis of different operating conditions of the PMSM drive system is presented, which highlights the system’s behaviour under various conditions. The presented algorithm enables the fast detection and compensation of failures with very short detection times, making it a versatile solution for a PMSM drive system and improving its overall reliability.

In this work, the problem of adaptive neural control for nonlinear systems with input nonlinearities and sensor fault in non-strict feedback form is addressed. Input saturation and dead-zone exist in the nonlinear system simultaneously. The impact of external disturbances and sensor fault in nonlinear systems is considered. To approximate the unknown functions in the system, radial basis function neural networks are used. An adaptive neural controller is developed by using the approximation ability of the neural networks and the backstepping method. Based on Lyapunov stability theory, the proposed control method ensures that all signals in the closed-loop system are semi-globally uniformly ultimately bounded and that the output of the system follows the reference signal within a bounded error. Finally, to demonstrate the effectiveness of the proposed control method, one numerical example and a real-life example about an electromechanical system are provided. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the adaptive fault‐tolerant control problem for a general nonlinear discrete‐time SISO system with unknown system model and sensor fault. First, utilizing the input‐output (I/O) data, an equivalent full‐form dynamic linearization (FFDL) data model is to be constructed by introducing a pseudo‐gradient vector. Then, to estimate the system's actual output from the sensor measurements corrupted by unknown faults, a nonlinear autoregressive with external input neural network (NARXNN) is employed and well‐trained, by which the compensation of the fault signal can hence be derived indirectly. Based on the optimality criterion, an adaptive fault‐tolerant control (FTC) strategy is therefore proposed, which promises the convergence of tracking error and the boundedness of system signals. The effectiveness of the proposed FTC algorithm is illustrated by simulation results. [ABSTRACT FROM AUTHOR]

Standalone low-voltage DC (LVDC) microgrids have emerged as potential alternatives in the context of effective rural electrification. The factors of reduction in conversion costs, paradigm shift in voltage levels of domestic loads made LVDC Microgrids more preferable. Being driven by intermittent renewable sources and dynamic loading, the voltage control is quite crucial and challenging to ensure a stable LVDC microgrid. Furthermore, delivering the control objectives becomes very difficult in the presence of sensor faults. In order to address this issue of sensor faults, this work proposes an Extended Luenberger Observer-based Active Sensor Fault-Tolerant Control (ELO-ASFTC) scheme. The proposed control scheme consists of the aspects of fault detection and isolation (FDI), and control reconfiguration. The proposed control scheme achieved the voltage tracking objective of the LVDC microgrid even under the presence of a faulty DC bus voltage sensor. The performance of the proposed scheme is observed in handling different sensor fault scenarios, loading conditions and irradiance changes. The effectiveness of the proposed control scheme is studied analytically, through the simulation studies in MATLAB, and hardware experimentation on the in house LVDC microgrid test bed. [ABSTRACT FROM AUTHOR]

Unexpected faults that occur in the sensors of the single-phase grid-connected converter (SGC) may deteriorate the control performance ofthe grid current and dc-link voltage and pose risks to the connected distributed generation system. This article proposes an unknown input sliding mode observer (UI-SMO) based sensor fault identification and remediation strategy. Therein, an UI-SMO is established that integrates the observations of the grid current and dc-link voltage with the reconstruction of the unknown input (i.e., load condition of the SGC). Such an observer-based analytical redundancy generates the residuals that indicate the abnormalities of the sensors for fault identification. An adaptive fault-tolerant control (AFTC) strategy for the SGC is developed, where an automated control system reconfiguration with unknown input adaptation is seamlessly implemented by substituting the data reported by the faulty sensors with their observed values. Extensive simulation and experimental studies validate the effectiveness of the proposed UI-SMO-based sensor fault identification and AFTC strategy of the SGC. [ABSTRACT FROM AUTHOR]

Supply chilled water temperature (SCWT) is an important variable for the efficient and stable operation of heating, ventilation, and air conditioning (HVAC) systems. A precisely measured value ensured by the continuous reliability of the temperature sensor is essential for optimal control of an HVAC system because temperature sensor faults can affect the chiller operation and waste energy. Therefore, temperature sensor fault-detection strategies are imperative for maintaining a comfortable indoor thermal environment and ensuring the efficient and stable operation of HVAC systems. This study proposes a fault-detection method for an SCWT sensor using a virtual sensor based on a long short-term memory-autoencoder. The fault-detection performance is evaluated considering a case study under various sensor fault scenarios to evaluate changes in indoor thermal comfort and energy consumption after correcting sensor faults detected by the virtual sensor. The results verify excellent fault-detection performance in various fault scenarios (F-1 scores ranging from 0.9350 to 1.000). After correcting the SCWT fault, indoor thermal comfort is steadily maintained without additional energy consumption (indoor set-point temperature unmet hour reduced by a maximum of 105.7 hours, and energy consumption decreased by up to 1.8%).

This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated.

This paper proposes a novel methodology for trajectory tracking of unknown discrete-time nonlinear systems under sensor and actuator faults. The proposed approach combines a recurrent neural identifier trained online with well-known characteristics of sliding mode technique. This paper includes a stability analysis based on Lyapunov theory. Finally applicability of the proposed scheme is shown through simulation results for a three-phase induction motor whose mathematical model is considered unknown. Furthermore, the system is operated in the presence of unknown disturbances as well as sensor and actuator faults.