A Latency-Aware and Fault-Tolerant Framework for Resource Scheduling and Data Management in Fog-Enabled Smart City Transportation Systems

Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.

Active Fault Tolerant Nonsingular Terminal Sliding Mode Control for Electromechanical System Based on Support Vector Machine

Effective fault diagnosis and fault-tolerant control method for aeronautics electromechanical actuator is concerned in this paper.By borrowing the advantages of model-driven and data-driven methods,a fault tolerant nonsingular terminal sliding mode control method based on support vector machine(SVM)is proposed.A SVM is designed to estimate the fault by off-line learning from small sample data with solving convex quadratic programming method and is introduced into a high-gain observer,so as to improve the state estimation and fault detection accuracy when the fault occurs.The state estimation value of the observer is used for state reconfiguration.A novel nonsingular terminal sliding mode surface is designed,and Lyapunov theorem is used to derive a parameter adaptation law and a control law.It is guaranteed that the proposed controller can achieve asymptotical stability which is superior to many advanced fault-tolerant controllers.In addition,the parameter estimation also can help to diagnose the system faults because the faults can be reflected by the parameters variation.Extensive comparative simulation and experimental results illustrate the effectiveness and advancement of the proposed controller compared with several other main-stream controllers.

Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism

Readout errors caused by measurement noise are a significant source of errors in quantum circuits,which severely affect the output results and are an urgent problem to be solved in noisy-intermediate scale quantum(NISQ)computing.In this paper,we use the bit-flip averaging(BFA)method to mitigate frequent readout errors in quantum generative adversarial networks(QGAN)for image generation,which simplifies the response matrix structure by averaging the qubits for each random bit-flip in advance,successfully solving problems with high cost of measurement for traditional error mitigation methods.Our experiments were simulated in Qiskit using the handwritten digit image recognition dataset under the BFA-based method,the Kullback-Leibler(KL)divergence of the generated images converges to 0.04,0.05,and 0.1 for readout error probabilities of p=0.01,p=0.05,and p=0.1,respectively.Additionally,by evaluating the fidelity of the quantum states representing the images,we observe average fidelity values of 0.97,0.96,and 0.95 for the three readout error probabilities,respectively.These results demonstrate the robustness of the model in mitigating readout errors and provide a highly fault tolerant mechanism for image generation models.

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.

Graph Attention Residual Network Based Routing and Fault-Tolerant Scheduling Mechanism for Data Flow in Power Communication Network

For permanent faults(PF)in the power communication network(PCN),such as link interruptions,the timesensitive networking(TSN)relied on by PCN,typically employs spatial redundancy fault-tolerance methods to keep service stability and reliability,which often limits TSN scheduling performance in fault-free ideal states.So this paper proposes a graph attention residual network-based routing and fault-tolerant scheduling mechanism(GRFS)for data flow in PCN,which specifically includes a communication system architecture for integrated terminals based on a cyclic queuing and forwarding(CQF)model and fault recovery method,which reduces the impact of faults by simplified scheduling configurations of CQF and fault-tolerance of prioritizing the rerouting of faulty time-sensitive(TS)flows;considering that PF leading to changes in network topology is more appropriately solved by doing routing and time slot injection decisions hop-by-hop,and that reasonable network load can reduce the damage caused by PF and reserve resources for the rerouting of faulty TS flows,an optimization model for joint routing and scheduling is constructed with scheduling success rate as the objective,and with traffic latency and network load as constraints;to catch changes in TSN topology and traffic load,a D3QN algorithm based on a multi-head graph attention residual network(MGAR)is designed to solve the problem model,where the MGAR based encoder reconstructs the TSN status into feature embedding vectors,and a dueling network decoder performs decoding tasks on the reconstructed feature embedding vectors.Simulation results show that GRFS outperforms heuristic fault-tolerance algorithms and other benchmark schemes by approximately 10%in routing and scheduling success rate in ideal states and 5%in rerouting and rescheduling success rate in fault states.

Design and Test of Multibus Adapter System on a Chip for Fault Tolerant Computer Systems

In order to improve the system reliability and performance and to reduce the system cost, volume and weight, we have designed, fabricated and tested the multibus adapter system of a trimodular redundant fault tolerant computer system on a single chip of 5000 gate CMOS gate array. The design, fabrication and test of this single chip system will be discussed..

FAULT-TOLERANT INTEGRATED NAVIGATION SYSTEM BASED ON NEURONS

In this paper, the multisensor data fusion technique of a fault tolerant integrated navigation system is discussed. A neural approach for data fusion is proposed for multisensor integrated systems. The simulation results show that this neural approach for data fusion is feasible.

Fault tolerant control of electric pitch control system based on single current detection

In view of the current sensors failure in electric pitch system,a variable universe fuzzy fault tolerant control method of electric pitch control system based on single current detection is proposed.When there is single or two-current sensor fault occurs,based on the proposed method the missing current information can be reconstructed by using direct current（DC）bus current sensor and the three-phase current can be updated in time within any two adjacent sampling periods,so as to ensure stability of the closed-loop system.And then the switchover and fault tolerant control of fault current sensor would be accomplished by fault diagnosis method based on adaptive threshold judgment.For the reconstructed signal error caused by the modulation method and the main control target of electric pitch system,a variable universe fuzzy control method is used in the speed loop,which can improve the anti-disturbance ability to load variation,and the robustness of fault tolerance system.The results show that the fault tolerant control method makes the variable pitch control system still has ideal control characteristics in case of sensor failure although part of the system performance is lost,thus the correctness of the proposed method is verified.

Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

In this paper, a data-based fault tolerant control(FTC) scheme is investigated for unknown continuous-time(CT)affine nonlinear systems with actuator faults. First, a neural network(NN) identifier based on particle swarm optimization(PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network(PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation(HJBE) more efficiently.Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.

Active Fault Tolerant Control of a Class of Nonlinear Time-Delay Processes

Based on a nonlinear state predictor (NSP) and a strong tracking filter (STF), a sensor fault tolerant generic model control (FTGMC) approach for a class of nonlinear time-delay processes is proposed. First, the NSP is introduced, and it is used to extend the conventional generic model control (GMC) to nonlinear processes with large input time-delay. Then the STF is adopted to estimate process states and sensor bias, the estimated sensor bias is used to drive a fault detection logic. When a sensor fault is detected, the estimated process states by the STF will be used to construct the process output to form a 'soft sensor', which is then used by the NSP (instead of the real outputs) to provide state predictors. These procedures constitute an active fault tolerant control scheme. Finally, simulation results of a three-tank-system demonstrate the effectiveness of the proposed approach.

Fault tolerant control based on stochastic distribution via RBF neural networks

A new fault tolerant control（FTC） via a controller reconfiguration approach for general stochastic nonlinear systems is studied.Different from the formulation of classical FTC methods,it is supposed that the measured information for the FTC is the probability density functions（PDFs） of the system output rather than its measured value.A radial basis functions（RBFs） neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network.As a result,the nonlinear FTC problem subject to dynamic relation between the input and the output PDFs can be transformed into a nonlinear FTC problem subject to dynamic relation between the control input and the weights of the RBFs neural network approximation to the output PDFs.The FTC design consists of two steps.The first step is fault detection and diagnosis（FDD）,which can produce an alarm when there is a fault in the system and also locate which component has a fault.The second step is to adapt the controller to the faulty case so that the system is able to achieve its target.A linear matrix inequality（LMI） based feasible FTC method is applied such that the fault can be detected and diagnosed.An illustrated example is included to demonstrate the efficiency of the proposed algorithm,and satisfactory results have been obtained.

Fault Tolerant Control for Networked Control Systems with Packet Loss and Time Delay

In this paper,a fault tolerant control with the consideration of actuator fault for a networked control system （NCS） with packet loss is addressed.The NCS with data packet loss can be described as a switched system model.Packet loss dependent Lyapunov function is used and a fault tolerant controller is proposed respectively for arbitrary packet loss process and Markovian packet loss process.Considering a controlled plant with external energy-bounded disturbance,a robust H ∞ fault tolerant controller is designed for the NCS.These results are also expanded to the NCS with packet loss and networked-induced delay.Numerical examples are given to illustrate the effectiveness of the proposed design method.

ONLINE MODEL AND ACTUATOR FAULT TOLERANT CONTROL FOR AUTONOMOUS MOBILE ROBOT

A novel fault-tolerant adaptive control methodology against the actuator faults is proposed. The actuator effectiveness factors （AEFs） are introduced to denote the healthy of actuator, and the unscented Kalman filter （UKF） is employed for online estimation of both the motion states and the AEFs of mobile robot. A square root version of the UKF is introduced to improve efficiency and numerical stability. Using the information from the UKF, the reconfigurable controller is designed automatically based on an enhancement inverse dynamic control （IDC） methodology. The experiment on a 3-DOF omni-directional mobile robot is performed, and the effectiveness of the proposed method is demonstrated.

Model-Based Fault Tolerant Control for Hybrid Dynamic Systems with Sensor Faults

A model-based fault tolerant control approach for hybrid linear dynamic systems is proposed in this paper. The proposed method, taking advantage of reliable control, can maintain the performance of the faulty system during the time delay of fault detection and diagnosis (FDD) and fault accommodation (FA), which can be regarded as the first line of defence against sensor faults. Simulation results of a three-tank system with sensor fault are given to show the efficiency of the method.

On-Line Fault Diagnosis and Fault-Tolerant Operation of Modular Multilevel Converters –A Comprehensive Review

Modular multilevel converters(MMCs)have been one of the most broadly used multilevel converter topologies in industrial applications,particularly in medium-voltage motor drives and high-voltage dc power conversion systems.However,due to the utilization of large amount of semiconductor devices,the reliability of MMCs becomes one of the severe challenges constraining their further development and applications.In this paper,common electrical faults of the MMC have been summarized and analyzed,including open-circuit switching faults,short-circuit switching faults,dc-bus short-circuit faults,and single line-to-ground faults on the ac side.A thorough and comprehensive review of the existing online fault diagnostic methods has been conducted.In addition,fault-tolerant operation strategies for such various fault scenarios in MMCs have been presented.All the fault diagnosis and fault-tolerant operation strategies are comparatively evaluated,which aims to provide a state-of-the-art reference on the MMC reliability for future research and industrial applications.

Open-circuit Fault-tolerant Control Strategy Based on Five-level Power Converter for SRM System

Switched reluctance motor power converters are prone to open-circuit faults because it need to withstand large voltages and currents.Due to the small number of traditional asymmetrical half bridge topology switches,it is difficult to carry out fault tolerant control when power converters has an open-circuit fault,resulting in larger output torque ripple.This paper presents a five-level power converter based on the traditional asymmetric half-bridge power converter.The five-level topology has more switching states and can work in multi-level mode.Based on the topology,different excitation and demagnetization voltages can be choose at different speeds.A fault-tolerance strategy is developed to decrease the influence of the open-circuit fault.The five-level power converter has four switches per phase,and two of them will be used in one of the operating mode.So the remaining two of the switches can be used for safe backup,enabling fault-tolerant control when an open-circuit occur.Since each phase of the five-level power converter proposed in this paper is independent of each other,a reasonable control strategy can be used to avoid the unbalance of the midpoint potential.Finally,the topology and fault-tolerant strategy proposed in this paper are verified by simulation and experiment.

Fault tolerant motion planning based on joint torque limit for redundant manipulators

First, two fault tolerant planning algorithms with avoidance of joint static torque limit or joint dynamic torque limit are proposed respectively. The former is suitable for the low-speed manipulators, and the latter is suitable for the high-speed manipulators. These algorithms not only can insure manipulation tasks to lie within the fault tolerant workspace but also can avoid joint torque limit, and hence can insure a redundant manipulator to be. fault tolerant in both kinematical sense and dynamic sense. Then, the simulation examples for a planar 3R manipulator demonstrate the validity of these algorithms.

Sensor fault-tolerant observer applied in UAV anti-skid braking control under control input constraint

This paper proposes a method for addressing the problem of sensor fault-tolerant control (FTC) for anti-skid braking systems (ABSs). When the wheel velocity sensor of the ABS for unmanned aerial vehicles (UAVs) becomes faulty, wheel velocity failure and feedback instability may occur. Firstly, a fault diagnosis and isolation (FDI) method based on a sliding mode observer approach is introduced to detect and isolate the fault of the sensor. When the wheel velocity sensor is in healthy conditions, the observer works in a diagnosis mode. If faults occur in the sensor, it acts as a wheel velocity estimator. Secondly, an FTC strategy, adopting a feedback compensation structure, is designed with input control constraints. In addition, based on the FDI result, a terminal sliding mode (TSM) controller is designed to guarantee that slip-ratio tracks its appropriate reference values in situations where runways change conditions during landing. The control system switches automatically from control using a wheel velocity sensor to sensorless control mode, so the observer-based FTC scheme is established. It is logical that the ABS keeps observed-state and remains stable when the wheel velocity sensor is broken and during external disturbance. Finally, simulation results show the effectiveness of the proposed method. © 1990-2011 Beijing Institute of Aerospace Information.

Fault tolerant control for a robot collaborative system

A new fault-tolerant control scheme is proposed for a nonlinear collaborative system that contains two robot subsystems. When fault occurs in one subsystem, the fault-free subsystem is used to compensate the fault influence of the faulty one on the whole collaborative system. When the faulty subsystem could not repair itself or the repair process needs a long time, the controller of the fault-free subsystem is reconfigured using the fault diagnosis information and other measured infor- mation, leading to the fault tolerant control of the robot collaborative system. Simulations of fault tolerant control for the robot collaborative system show the effectiveness of the proposed method.

Fault tolerant synchronization of chaotic systems based on T-S fuzzy model with fuzzy sampled-data controller

In this paper the fault tolerant synchronization of two chaotic systems based on fuzzy model and sample data is investigated. The problem of fault tolerant synchronization is formulated to study the global asymptotical stability of the error system with the fuzzy sampled-data controller which contains a state feedback controller and a fault compensator. The synchronization can be achieved no matter whether the fault occurs or not. To investigate the stability of the error system and facilitate the design of the fuzzy sampled-data controller, a Takagi Sugeno （T-S） fuzzy model is employed to represent the chaotic system dynamics. To acquire good performance and produce a less conservative analysis result, a new parameter-dependent Lyapunov-Krasovksii functional and a relaxed stabilization technique are considered. The stability conditions based on linear matrix inequality are obtained to achieve the fault tolerant synchronization of the chaotic systems. Finally, a numerical simulation is shown to verify the results.