Fault-tolerant Model Research Articles

Event‐triggered estimation and model predictive control for linear systems with actuator fault

This study is concerned with the design of an event-triggered fault estimation and fault tolerant model predictive control scheme for linear systems subject to disturbances and actuator faults by using the method of delta operator. First, an observer is established to estimate the state and the fault simultaneously. An H 2 / H ∞ model predictive controller with fault compensation signals is then proposed through solving a constrained optimisation problem. An event-triggered scheme is employed to conserve the network bandwidth. The new design techniques are shown to be effective via simulation results.

Universal fault-tolerant measurement-based quantum computation

Certain physical systems that one might consider for fault-tolerant quantum computing where qubits do not readily interact, for instance photons, are better suited for measurement-based quantum-computational protocols. Here we propose a measurement-based model for universal quantum computation that simulates the braiding and fusion of Majorana modes. To derive our model we develop a general framework that maps any scheme of fault-tolerant quantum computation with stabilizer codes into the measurement-based picture. As such, our framework gives an explicit way of producing fault-tolerant models of universal quantum computation with linear optics using protocols developed using the stabilizer formalism. Given the remarkable fault-tolerant properties that Majorana modes promise, the main example we present offers a robust and resource efficient proposal for photonic quantum computation.

Enhanced Fault-Tolerant Model Predictive Current Control for a Five-Phase PM Motor With Continued Modulation

To deal with the single-phase open-circuited fault, an enhanced fault-tolerant model predictive current control with continued modulation is proposed in this article. This method includes reconstructing the postfault vector distribution and adopting a multistep vector selection method. In the reconstruction procedure, the transition vectors are generating to realize standard pulsewidth modulation waveforms. Then, the unreachable area can be filled by creating new synthetic vectors. Finally, the vectors with oversized amplitudes are optimized by using null vectors. In order to cover the full modulation area, a multistep vector selection method is used to determine the phase angle and amplitude of the optimal vector. The phase angle can be determined by defining two cost functions and adopting duty cycle modulation technology. The amplitude of the optimal vector can be optimized by utilizing the duty cycle modulation technology again. The oscillation, due to the back electromotive force, is compensated in each step. A thorough experimental evaluation has been conducted to confirm the effectiveness and superiority of the proposed fault-tolerant method.

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle.

The fault detection and isolation are very important for the driving safety of autonomous vehicles. At present, scholars have conducted extensive research on model-based fault detection and isolation algorithms in vehicle systems, but few of them have been applied for path tracking control. This paper determines the conditions for model establishment of a single-track 3-DOF vehicle dynamics model and then performs Taylor expansion for modeling linearization. On the basis of that, a novel fault-tolerant model predictive control algorithm (FTMPC) is proposed for robust path tracking control of autonomous vehicle. First, the linear time-varying model predictive control algorithm for lateral motion control of vehicle is designed by constructing the objective function and considering the front wheel declination and dynamic constraint of tire cornering. Then, the motion state information obtained by multi-sensory perception systems of vision, GPS, and LIDAR is fused by using an improved weighted fusion algorithm based on the output error variance. A novel fault signal detection algorithm based on Kalman filtering and Chi-square detector is also designed in our work. The output of the fault signal detector is a fault detection matrix. Finally, the fault signals are isolated by multiplication of signal matrix, fault detection matrix, and weight matrix in the process of data fusion. The effectiveness of the proposed method is validated with simulation experiment of lane changing path tracking control. The comparative analysis of simulation results shows that the proposed method can achieve the expected fault-tolerant performance and much better path tracking control performance in case of sensor failure.

A New Intelligent Biologically-Inspired Model for Fault Tolerance in Distributed Embedded Systems

The objective of this work is to present a new heuristic for solving the problem of fault tolerance in real time distributed embedded systems. The proposed idea is to model the distributed embedded architecture inspiring from the rennin-angiotensin aldosterone (RAAS) biological system which plays a major role in the pathophysiology of the cardiovascular system, from the point of view of pressure regulation and vascular, cardiac and nephrological remodeling. The proposed heuristic deals with uncertain information on a set of periodic tasks that run on multiple processors and satisfies certain temporal and energetic constraints from which the scheduling and the distribution of these tasks on the different processors are performed. In order to respect the energy constraints, this article proposes the introduction of energy consumption at the dynamic task scheduling level by using the dynamic voltage scaling (DVS) technique. The authors have seen that the introduction of a detection/prevention mechanism against potential errors in the proposed algorithm is a must for good results.

Fault‐tolerant predictive trajectory tracking of an air vehicle based on acceleration control

A novel fault-tolerant model predictive control (MPC)-based trajectory tracking approach for an aerial vehicle is presented in this study. A generalised online sequential extreme learning machine is introduced first to identify the corresponding coefficients of actuator faults. Subsequently, a robust trajectory tracking control is developed based on MPC, where the system constraints can be effectively considered in the designed control scheme. Trajectory tracking control is achieved by controlling only the acceleration of the aerial robot in the MPC structure. This leads to less computational burden and faster closed-loop dynamics. In addition, an effective disturbance observer is employed, which can satisfactorily capture the effects of unmodelled dynamics and external disturbances on the system dynamics. The stability of the proposed control algorithm is proved based on the Lyapunov theory under realistic assumptions. Finally, the proposed control strategy is applied to a quadrotor unmanned aerial vehicle. Both simulation and experimental results are presented to demonstrate the effectiveness of the introduced fault-tolerant control scheme. The obtained results suggest that the designed control approach is capable of stabilising the attitude and providing acceptable performance in tracking a predefined trajectory in a three-dimensional environment in the simultaneous presence of actuator faults and external disturbances.

Dependable power extraction in wind turbines using model predictive fault tolerant control

In this paper, a fault tolerant model predictive control scheme is proposed for wind turbines in the partial-load region to meet the control objectives in the presence of disturbances, uncertainties, sensor and actuator faults. The aim of wind turbine control systems in the partial-load region is to capture maximum power by tracking the optimal generator speed. But any fault in the sensors and actuators can take away the closed-loop system from the main objectives and maybe make the system unstable in some cases. At first, an online model predictive controller (MPC) is designed as a nominal controller to track the maximum power and guarantee all constraints satisfaction without considering any fault. In the next step, an adaptive sliding mode observer (SMO) is designed to estimate the actual states and sensor faults, simultaneously. Finally, an additive control law is represented and shown that it is able to tolerate the actuator faults effectively. Using extensive simulation results it is shown that the proposed strategy is able to handle the uncertainties, sensor and actuator faults in the control system, simultaneously.

NLFFT: A Novel Fault Tolerance Model Using Artificial Intelligence to Improve Performance in Wireless Sensor Networks

A wireless sensor network (WSN) is a collection of various tiny devices known as sensor nodes, which are also called motes. Due to high-energy consumption, the possibility of hardware, link or node failure, and some malicious attacks, sensor networks are considered error-prone networks. Hence, fault tolerance (FT) in WSN is one of the prominent issues. This article presents a novel FT approach named node-link failure fault tolerance model (NLFFT Model) in WSN, to handle the faults that occur either by link or node failure during data transmission from the sensor to the sink or base station. The NLFFT model consists of an improved quadratic minimum spanning tree (Imp-QMST) approach. This approach helps in finding the alternate link whenever it fails due to various situations and also an improved-handoff (Imp-Handoff) algorithm to support the node failure to the fault tolerance. Improved QMST presents a novel mechanism to find an alternate edge in place of the broken or failed edge in the spanning tree, to improve the fault tolerance in WSN. Imp-Handoff suggests a novel way to find the faulty node owing to less battery power and replaces a defective node by an appropriate neighbor to shift the tasks performed by a faulty node in WSN. Simulation results clearly state that as compared to the basic techniques i.e. Q-MST and Handoff algorithm, the proposed NLFFT model improvises the performance of WSN around by 7%. The results prove that the Imp-QMST gives about 6% improved throughput, 5% less end-to-end delay, and 6% less power consumption than the QMST algorithm. Similarly, Imp-Handoff improves about 4% throughput, 6% less end-to-end delay, and utilizes 7% less power consumption.

Active Fault Tolerant Control of Adaptive Cruise Control System Considering Vehicle-Borne Millimeter Wave Radar Sensor Failure

Vehicle-borne millimeter wave radar sensor may malfunction during signal acquisition and transmission process, which will affect the decision of adaptive cruise control (ACC) system and the safe driving of vehicles. However, adding other redundant environment-aware devices will increases costs. Therefore, in this paper, an active fault tolerant control of ACC system considering vehicle-borne millimeter wave radar sensor failure is proposed. Sensor faults are taken as discrete events, and the mixed logical dynamical (MLD) model of ACC upper control system is built which includes both the fault-free dynamics and the fault dynamics of the system. Then, the active fault tolerant control model of ACC system is established based on model predictive control (MPC) framework. Compared with the existing researches, this work emphasizes on the active fault tolerant control without adding other redundant environment-aware devices, which has not been fully revealed by the existing researches and is important to the industrial application. Moreover, the active fault tolerant control method can be easily ported to other driver assistance system besides ACC. The simulation results show that the vehicle equipped with the active fault tolerant ACC system can still drive safely and smoothly without being affected by radar sensor failures, which demonstrates its great significance to improve vehicle’s own intelligence and ensure the safe driving instead of relying entirely on sensors.

Fault Tolerance in Grid Computing by Resource Clustering

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. At this scale, the characteristics of dynamicity, resource heterogeneity and scalability have made fault tolerance more complex. In this paper, we propose FT-GRC a fault tolerance model that seeks to find the best substitute for the failed node by the clustering of the grid resources. This model is based on dynamic coloured graphs without replication of computer resources. The proposed fault tolerance mechanism uses scoring function to determine the appropriate substitute for each failed node by calculating the performance level of each node, and later exploits clustering to determine optimally the choice of substitute. Experimental results show the efficiency of the scoring method and the gain obtained by looking for the substitutes in the same cluster and then by the research for the nearest substitutes.

QoS-Aware Task Placement With Fault-Tolerance in the Edge-Cloud

The geographically dispersed resources and ever-changing context incur unique heterogeneity, potential fragility, and vulnerability of an edge-cloud system. Thus, the reliability guarantee of services in the edge-cloud is critical. This paper firstly proposes a QoS-aware scheduling model with fault-tolerance in the edge-cloud, which extends the traditional primary-backup (PB) fault-tolerant model to improve the service reliability in the edge-cloud with the time constraints of tasks being satisfied. Then, a QoS-aware fault-tolerant scheduling algorithm including primary copy placement, backup copy placement and an adjustment mechanism is proposed to improve the QoS levels of tasks in the edge-cloud. The primary copy placement is to guarantee the earlier execution of the primary copy of a task to better satisfy the time requirements of tasks. The backup copy placement is to ensure the later execution of the backup copy of a task, reducing the overlapping of the two copies of a task, realizing the improvement of the resource utilization in the edge-cloud under the condition of redundancy and deadline requirements of tasks. The adjustment mechanism is triggered to rearrange the task copies of a computing node of the edge-cloud after the deallocation of a backup copy on the node, to better assist the goal-achievement of the primary and backup copy scheduling. Finally, through extensive simulation experiments with the real world taxi traces, the performance difference between the proposed method and the other four methods are evaluated. Results show that the proposed method generally outperforms the other methods in terms of guarantee ratio, average QoS level, and reliability cost.

Adaptive fault-tolerant model for improving cloud computing performance using artificial neural network

Cloud computing is a paradigm that has greatly contributed to the emergence of new applications of IT services. However, the design of dependable and efficient cloud architectures requires a high knowledge of the recurring failures causes which compromise the efficient functioning of the system. Nowadays, the number of datasets recording the real state of cloud systems still rarely available to the public. This article uses failure data published by Backblaze which is one of the largest data storage providers. This dataset consists of the operating status of heterogeneous servers that were collected during the period between January 2015 and December 2018. Then, the data has been filtered and preprocessed to keep a total of 2,878,440 records including 128,820 failures. We investigate the correlation between hard drive parameters and failure ​​by exploiting the attributes of Self-Monitoring, Analysis and Reporting Technology (SMART). Then, we investigate the predictive capabilities of five machine learning models including naïve Bayes and artificial neural networks (ANN) to define a failure prediction module for the cloud architecture. The experimental results demonstrate that the artificial neural network (ANN) model offers the best prediction accuracy.

Faulty node modeling and diagnosis in interconnection networks (extended abstract)

Faulty processing node analysis, in particular, self-diagnostic paradigm, is an important topic in the area of interconnection network studies. Several diagnostic models have been introduced, with the PMC and MM* models being two of the most popular ones. Researchers have also proposed various extensions and enhancements of fault-tolerance models to better capture the distribution, and identification, of faulty nodes in realistic scenarios. One of them is the g-extra fault-tolerance model, where each cluster in a network with faulty nodes contains at least g+1 fault-free nodes. This paper, following an analytical and constructive approach based on applied graph theory, suggests a general process to identify the maximum number of faulty nodes in a network in terms of the g-extra fault-tolerance model, and as a demonstrative example, provides a specific result for the (n, k)-star graphs.

A Fault-Tolerant Approach to Alleviate Failures in Offloading Systems

Computation offloading effectively expands the usability of mobile terminals beyond their physical limits, and also greatly extends their battery charging intervals. Offloading or cyber foraging is a technique by which large and complex computational jobs are relocated from lightweight portable devices (such as smartphones) called offloadee to powerful servers (such as nearby laptops/desktops or cloud server over the Internet) called surrogates, and getting the output back at the offloadee. Many research works have been done so far on the architecture of offloading systems, but only few works can be found on fault detection and tolerance. So this paper concentrates on categorizing different failures that may affect the benefit of offloading computation and proposes a fault-tolerance approach to alleviate those faults. The checkpoint based fault-tolerance approach proposed in this paper is able to handle crash, omission and transient failures altogether. Fault prevention is obtained by utilizing historical data for choosing worthy surrogate from known neighborhood. This fault tolerance model is evaluated by applying a real application named $$\pi$$calculator and Scimark benchmark suite. The system is implemented and the proposed approach is found to be effective with respect to energy consumption and resource utilization even in case of crash and omission failure.

Study on dual redundant PMSM drive design and fault-tolerant control strategy

ABSTRACTWith the development and application of servo system, the requirements of high performance servo system are getting higher and higher. Its key is the high-performance motor that has the fault-tolerance to improve the motor reliability. This paper proposes a novel dual redundant PMSM (Permanent magnet synchronous motor), which has multiple same armature winding. Each winding has same three phases and is controlled by inverter module. Based on the fault-tolerant control system model, a reliable fault-tolerant control strategy has been proposed. The experiment platform is built to carry out consistency experiment of each redundancy, response characteristic experiment and fault-tolerant experiment of two redundancies in the PMSM. The experiment result shows the dual redundant PMSM has a good response and fault-tolerance characteristics.

Fault-tolerant model predictive control of a de-manufacturing plant

This paper describes the efficient implementation of a model predictive control (MPC) algorithm for the management of the pallets loaded on the transportation line of a de-manufacturing plant. In order to reduce the computational burden required for the solution of the online optimization problem, and make it compatible with industrial applications, different control and prediction horizons are used. In this way, the complexity of the optimization problem is reduced without significantly affecting the performance of the plant. In addition, a detailed inspection of the transportation line configuration, and the parallelization of the optimization and implementation tasks, allows one to obtain computational times fully comparable to those of simple heuristic rules but with significant improvements in terms of the plant throughput. In the second part of the paper, a fault detection procedure is developed for the identification and isolation of sensors’ and actuators’ faults. Then, the basic MPC algorithm is modified to obtain a control scheme tolerant to instrumentation faults. Both simulation and experimental results are reported and discussed to show the control performance and the practical applicability of the proposed approach.

Fault-tolerance model of the information systems

The article deals with the reliability problem of information systems and contains the developed method for assessing the fault-tolerance of data transmission systems and mathematical model that allows to obtain the probability of failure at different levels of information systems.

A New Energy-Efficient and Fault-Tolerant Evolution Model for Large-Scale Wireless Sensor Networks Based on Complex Network Theory

In this article, the authors present a new novel energy-efficient and fault-tolerant evolution model for large-scale wireless sensor networks based on complex network theory. In the evolution model, not only is the residual energy of each node considered, but also the constraint of links is introduced, which makes the energy consumption of the whole network more balanced. Furthermore, both preferential attachment and random attachment to the evolution model are introduced, which reduces the proportion of the nodes with high degree while keeping scale-free network characteristics to some extent. Theoretical analysis shows that the new model is an extension of the BA model, which is a mixed model between a BA model and a stochastic model. Simulation results show that EFEM has better stochastic network characteristics while keeping scale-free network characteristics if the value of random probability is near 0.2 and it can help to construct a high survivability network for large-scale WSNs.

Fault-Tolerant Predictive Control of Six-Phase PMSM Drives Based on Pulsewidth Modulation

Multiphase machines are famous for their fault-tolerant capability. Conventional fault-tolerant model predictive current control (FT-CMPC) of multiphase machines suffers from high computational burden, low switching frequency, and relatively high current harmonics. This paper proposes a fault-tolerant PWM predictive control (FT-PWMPC) to enhance the performance of fault-tolerant predictive control strategy without using complex enumeration method. The reference phase voltages are generated by a postfault mathematical model in stationary reference frame and transformed to duty cycles with a properly set common-mode voltage. Then, a comparative study of fault-tolerant predictive control strategies is presented, including FT-CMPC, FT-two-vector-based-predictive control, and the proposed FT-PWMPC. The control strategies are implemented with the same sampling frequency or switching frequency on a six-phase permanent magnet synchronous machine test bench. Experimental results of dynamic response, steady-state currents, vibration, and parameter sensitivities are given to identify the advantages and limitations of each control strategy.

Trust aware fault tolerant prediction model for wireless sensor network based measurements in Smart Grid environment

Reliable and accurate prediction models for power generation, transmission and distribution can radically transform the Smart Grid (SG) environment. The application of wireless sensor networks in SG is among the latest areas of research. In this paper, a resilient Time Series Trust Model (TSTM) has been implemented in wireless sensor nodes that are modeled as smart meters in power generation and consumption. The performance of the proposed trust model has been compared with four other models. Non-linear auto regressive trust based prediction models for power generation and power consumption has also been proposed based on five different algorithms namely treepartition, wavenet, sigmoidal, feed forward net and cascade forward net. The prediction accuracy of these models are evaluated based on suitable metrics. The resilience of the proposed trust model is validated in the presence of offset fault and data loss fault in smart meters. The proposed TSTM emerges as the most robust and fault tolerant model.