Fault-tolerant Scheme Research Articles

Enabling the execution of HPC applications on public clouds with HPC@Cloud toolkit

AbstractThe advent of cloud computing has made access to computing infrastructure available to millions of users that face resource constraints. In the context of high performance computing (HPC), public cloud resources have emerged as a cost‐effective alternative to expensive on‐premises clusters. However, there are several challenges and limitations in adopting this approach. This paper proposes HPC@Cloud , a provider‐agnostic open‐source software toolkit that facilitates the migration, testing, and execution of HPC applications in public clouds. The toolkit takes advantage of various fault tolerance technologies to enable the use of inexpensive transient cloud infrastructure, commonly known as “spot” instances. Also, it features integration with singularity containers, allowing users to run complex applications on virtual HPC clusters in a portable and reproducible way. Finally, it provides a data‐based empirical approach to estimating cloud infrastructure costs for HPC workloads. The results obtained on two public cloud providers (AWS and Vultr) show that: (i) HPC@Cloud can efficiently build virtual HPC clusters on the cloud; (ii) the new adaptive fault tolerance strategy outperforms other existing strategies based on blocking restoration; (iii) the integration of singularity containers into HPC@Cloud improves the portability of HPC applications to public clouds with negligible performance penalty to the applications; (iv) the proposed cost prediction approach can estimate the cost of running the applications on AWS and Vultr with up to 93% accuracy on average.

Fixed time adaptive fault tolerant sliding mode control of PEMFC air supply system

Guaranteeing rapid management of oxygen excess ratio (OER) for proton exchange membrane fuel cells (PEMFC) under load variations is a challenge with practical significance. To improve the controller response of PEMFC air supply system, a fixed time fault-tolerant control strategy is proposed in this paper. A fixed convergence time of PEMFC air supply control system is quantified and guaranteed by the proposed method and the control response can be accelerated by reducing the setting time. The contributions of this paper is fourfold. Firstly, the manifold leakage fault is estimated by fixed time fault observer, which can improve the real-time fault tracking performance of the control system. Secondly, a hierarchical fixed time sliding mode controller is proposed to regulate the OER rapidly. Furthermore, a novel adaptive term is employed to reduce the effects of the chattering from fault observer in the outer loop. Finally, the effectiveness of the proposed method is verified on a real-time PEMFC simulation platform. Experimental results on a Hardware-in-the-loop (HIL) platform show that the proposed fixed time fault tolerant control strategy has the characteristics of fast response and high robustness.

Microservices enabled bidirectional fault-tolerance scheme for healthcare internet of things

Microservices enabled bidirectional fault-tolerance scheme for healthcare internet of things

A Fault-Tolerant Control Strategy for Three-Level Grid-Connected NPC Inverters after Single-Arm Failure with Optimized SVPWM

Three-level NPC inverters have been widely used in grid-connected systems due to their superior performance compared with two-level inverters, but more switches lead to high fault probability. Meanwhile, the neutral point potential (NPP) fluctuation of the DC link is an inherent problem of three-level NPC inverters. To keep the three-level NPC inverter running stably after single-arm failure, a fault-tolerant control strategy based on an optimised space vector pulse width modulation (SVPWM) is proposed in this paper. Firstly, the common-mode voltage (CMV) of the postfault three-level NPC inverter is analysed and then the preliminary synthesis principles of the reference voltage vector are determined. Then, in order to ensure the NPP balance and the quality of the grid-connected currents, the reference voltage vector synthesis rules are optimised, a low-pass filter (LPF) and a hysteresis comparator are designed, respectively, to ensure the quality of grid-connected currents and effectively decrease the DC link NPP deviation. Finally, the simulation results show that the proposed fault-tolerant control strategy can realize the stable and reliable operation of the grid-connected three-level NPC inverter after single-arm failure, and the CMV can be reduced significantly, the quality of grid-connected currents is also improved. The proposed fault-tolerant strategy also shows good performance when the grid-connected currents change.

Co-design of a multirotor UAV with robust control considering handling qualities and motor failure

Over the last few decades, multirotor UAVs have become significantly popular because of their various applications. However, multirotors suffer from poor endurance performance as compared to other aircraft configurations with lifting surfaces. This limitation can be addressed by improving the technology of components or by developing design optimization approaches at the vehicle level. This paper presents a co-design approach for the multirotor structure and propulsive system to improve the vehicle's energetic efficiency while respecting dynamic performance requirements. For this purpose, the approach uses a bi-level formulation to optimize the design and control concurrently. The methodology offers the possibility to activate or not a passive fault tolerant strategy in the controller synthesis to be tolerant to single rotor failure. In contrast to conventional design approaches that sequentially design each part, the co-design approach leads to a better optimal solution by considering the interactions and couplings between design and control. With the proposed co-design approach, the results show the soundness of the proposed design methodology and a significant reduction in the consumed energy compared to a reference non-optimal design while maintaining the same requirements in terms of dynamic performance.

Fault estimation and tolerant bumpless transfer control for switched systems

A novel fault estimation and tolerant bumpless transfer control strategy is proposed for switched systems. In this control scheme, a switched estimator, a switched estimator-based tolerant bumpless transfer controller and a switching logic are jointly designed. The estimator relaxes the assumption on the usual observer matching condition in the fault estimation issue of switched linear systems. The controller can tolerate the fault with small input bumps at switching time. The switching law is an improvement of the traditional state-dependent switching law with dwell time still maintained, permitting the increase of the Lyapunov function for subsystems within the ensured dwell time interval. Through the presented control scheme, a criterion is developed to drive the solvability of the fault estimation and tolerant bumpless transfer control issue for the SSs. Also, with this criterion, this control issue of subsystems can be unsolvable. An example of regulating the current for a chua’s circuit system is provided for purpose of verifying the theoretical result.

Time-varying formation tracking for multi-agent systems with maneuvering leader under DDoS attacks and actuator faults

Time-varying formation tracking problems for multi-agent systems (MASs) under distributed Denial-of-Service (DDoS) attacks and actuator faults are studied. To deal with the hybrid threats at the cyber and physical layers, an estimator-based fault-tolerant hierarchical control scheme is introduced, which is applicable to channel-wise asynchronous communication. Sufficient conditions for formation tracking of maneuvering leader with ultimately bounded error are obtained, and the particular case of periodic communication and attacks with constrained duration and frequency is further analyzed. Comparative physical simulations based on ROS and Gazebo are first conducted to show the resilience of our scheme against the threats. Finally, an experimental platform containing DJI Tello quadrotors and a self-developed ground control station is built, based on which practical experiments with four quadrotors are carried out to evaluate the effectiveness and engineering practicability of the proposed control framework.

Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture

The deployment of distributed multi-controllers for Software-Defined Networking (SDN) architecture is an emerging solution to improve network scalability and management. However, the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low resilience. Thus, we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane failures. This fault-tolerance strategy has been applied to distributed SDN control architecture, which allows each switch to migrate to next controller to enhance network performance. In this paper, the Reliable and Dynamic Mapping-based Controller Placement (RDMCP) problem in distributed architecture is framed as an optimization problem to improve the system reliability, quality, and availability. By considering the bound constraints, a heuristic state-of-the-art Controller Placement Problem (CPP) algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular controllers. The algorithm identifies the optimal controller location, minimum number of controllers, and the expected assignment costs after failure at the lowest effective cost. A metaheuristic Particle Swarm Optimization (PSO) algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and cost-effectiveness. The effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline algorithms. The findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.

Performance Optimization and Fault-Tolerance of Highly Dynamic Systems Via Q-Learning With an Incrementally Attached Controller Gain System.

High-performance and reliable control of systems that are highly dynamic and open-loop unstable is challenging but of considerable practical interest. Thus, this article investigates the performance optimization and fault tolerance of highly dynamic systems. First, an incremental control structure is proposed, where a controller gain system is attached to the predesigned controller, and by reconfiguring the controller gain system, the performance can be equivalently optimized as configuring the predesigned one. The incremental attachment of the controller gain system does not modify the existing control system, and it can be easily attached via various communication channels. Second, a structure integrating fault-tolerance strategy and hardware redundancy is proposed. Under this structure, command fusion and fault-tolerance strategies are developed where the control commands from different control units are optimally fused, and each control unit can be reconfigured w.r.t. the performance of the other ones. Furthermore, Q -learning algorithms are developed to realize the proposed structures and strategies in real-time model-freely. As such, varying operational conditions of the highly dynamic system can be tackled. Finally, the proposed structures and algorithms are validated case by case to show their effectiveness.

Fault Tolerant Brushless DC Motor Drive for Aerospace Applications

This article brings out a Fault-tolerant BLDC motor drive for aerospace applications using the redundancy concept. In a way, it brings out a fault-tolerant strategy that can be used to continue the regular operation of a BLDC motor drive even after the occurrence of faults. As BLDC motors are used in critical and dangerous control areas like military services and space vehicles, a fault-tolerant drive is essential to maintain drive operation and provide desirable output. This article compares fault simulation results in the software model of a BLDC motor drive to those of fault simulation results in hardware for three main types of faults. Fault simulation is carried out for three types of faults, viz. inverter device open circuit fault, motor winding open circuit fault, and rotor position sensor (hall sensor) open-circuited fault. Fault tolerance is ensured by introducing a redundant drive (drive-2), which operates the complete drive at the advent of any of the faults mentioned above in the main (healthy) drive-1. A fault-tolerant (redundant) hardware comprising dual stator BLDC motor and redundant controllers is realized and operationalized. Fault simulation is carried out in this hardware, and these results are validated with the results of fault simulation in the MATLAB SIMULINK model. Software and hardware results are comparable and form a basis for developing fault-tolerant electro-mechanical actuation systems for high-reliability, high-cost applications, mainly aerospace.

Adaptive Iterative Learning Fault-Tolerant Consensus Control of Multiagent Systems Under Binary-Valued Communications.

In this article, the iterative learning averaging consensus problem is studied for multiagent systems with system uncertainties, actuator faults, and binary-valued communications. Considering only binary-valued measurement information with stochastic noise can be received from its neighbors for each agent, a new two-iteration-scale framework that alternates estimation and control is designed. Under the proposed framework, each agent estimates the neighbors' states based on the empirical measurement method during a dwell iteration interval, during which each agent's states will keep constant along the iteration axis. Further, in view of the impacts of system uncertainties and actuator faults, a novel adaptive iterative learning fault-tolerant averaging consensus control scheme is designed based on its own states and the estimated neighbors' states. Finally, the resulting closed-loop system is rigorously proved to be stable, and numerical simulations are conducted to demonstrate the effectiveness of the developed control strategy.

I&I adaptive fault-tolerant safe control of safety-critical systems

This paper proposed an Immersion and Invariance (I&I) adaptive fault-tolerant safe control scheme for a class of safety-critical (SC) systems with parametric faults. The scheme combines I&I Adaptive Control Lyapunov Function (I&I aCLF) with I&I Adaptive Control Barrier Function (I&I aCBF) to shape the transient performance of constrained states. Forward invariance of a safe set is achieved through online parameter adaptation, and the parameter convergence is guaranteed through I&I adaptation law. The conservatism is reduced by both shaping transient performance and ensuring parameters convergence. A SC controller guaranteeing stability and safety is obtained by solving an optimization problem with safety constrain, and an analytical solution is provided to facilitate the calculation and execution of the SC controller. A numerical simulation is carried out to illustrate the effectiveness of the proposed method.

I&I adaptive fault-tolerant control of a class of nonaffine systems with nonlinearly parameterised faults

This paper provides a novel fault-tolerant control scheme for a class of nonaffine systems with nonlinearly parameterised (NLP) faults. The nonaffine system is firstly transformed into an augmented one via dynamic feedback control. A fault-tolerant controller with Immersion and Invariance (I&I) adaptation law is designed for the transformed system through dynamic surface control. The controller avoids complexity due to the explosion of terms in backstepping design. The I&I adaptation law is developed to recover the unknown parameters of NLP faults in the system. The proposed scheme can shape the transient performance of the parameter estimation error and tracking error. In the tracking problem, all the signals and tracking error converge exponentially to a small neighbourhood of the origin. In the regulation problem, the system output converges exponentially to zero. A numerical simulation is carried out to verify the effectiveness of the proposed scheme.

Restorable Shortest Path Tiebreaking for Edge-Faulty Graphs

The restoration lemma by Afek et al. [ 3 ] proves that, in an undirected unweighted graph, any replacement shortest path avoiding a failing edge can be expressed as the concatenation of two original shortest paths. However, the lemma is tiebreaking-sensitive : if one selects a particular canonical shortest path for each node pair, it is no longer guaranteed that one can build replacement paths by concatenating two selected shortest paths. They left as an open problem whether a method of shortest path tiebreaking with this desirable property is generally possible. We settle this question affirmatively with the first general construction of restorable tiebreaking schemes . We then show applications to various problems in fault-tolerant network design. These include a faster algorithm for subset replacement paths, more efficient fault-tolerant (exact) distance labeling schemes, fault-tolerant subset distance preservers and + 4 additive spanners with improved sparsity, and fast distributed algorithms that construct these objects. For example, an almost immediate corollary of our restorable tiebreaking scheme is the first nontrivial distributed construction of sparse fault-tolerant distance preservers resilient to three faults.

Anti-disturbance control for HVs system subject to nonlinear actuator characteristics with extended state observer

This paper proposes a radial basis function neural network and the actuator fault extended state observer based fast non-terminal sliding mode fault-tolerant control scheme for hypersonic vehicles (HVs) system. The proposed scheme can cope with actuator faults, actuator saturation, parameter uncertainties, and external disturbances for HVs systems, without relying on redundancy as some passive FTC methods do. Moreover, an anti-windup compensator is designed to mitigate the input saturation effects. Furthermore, the finite-time stability of the attitude angle of the HVs system is proved by Lyapunov's theorem. Numerical simulations validate the effectiveness of the proposed scheme.

Predefined-Time Adaptive Fault-Tolerant Control for Switched Odd-Rational-Power Multi-Agent Systems

This paper studies the distributed adaptive neural network (NN) predefined-time (PT) fault-tolerant consensus tracking control for unknown non-strict feedback nonlinear multi-agent systems (MASs) with time-varying full state constraints (TVFSCs), arbitrary switching behavior functions (possibly asynchronous), actuator faults and high powers. Compared with existing results, the convergence time estimation bound in this paper is less conservative due to the upper bound of convergence time presents as a tuning parameter. This paper avoids the control gain of each virtual controller to appear in the next virtual controller by using separable function technique, so it can reduce the control action complexity. Besides, this paper does not use any filters or piecewise continuous function in the controller design process, which can effectively avoid the potential singularity problem. An adaptive NN fault-tolerant consensus tracking controller is designed via backstepping technique and tangent barrier Lyapunov functions (BLF-Tans), which can ensure that all the closed-loop signals are bounded within a PT and the TVFSC bounds are not violated. Finally, numerical simulation and practical example further verify the effectiveness of the presented control algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Many practical (underactuated, weakly coupled and unstable mechanical systems) control systems can be modeled as uncertain high-order MAS with high powers. Due to the influence of environment and other factors, the values of some variables in the practical systems are limited and cannot be arbitrarily selected. In addition, the actual systems may fail due to human factors and in most of the relevant literatures, it is often assumed that the powers are equal to 1 and only achieve the controlled systems asymptotic stability as time approaches infinity. Therefore, in this paper, an adaptive PT fault-tolerant control scheme is developed for MASs with high powers, time-varying full state constraints and actuator faults. The effectiveness of the control scheme is illustrated based on simulation study.

Fixed-Time Prescribed Performance Consensus Control for Multiagent Systems With Nonaffine Faults

This paper studies the fixed-time consensus tracking control of nonlinear multi-agent systems (NNMASs) suffering from non-affine faults. A fixed-time command filter is constructed to solve the “explosion of complexity” issue, and a novel fixed-time error compensation mechanism is designed to eliminate filtering errors. The adaptive fuzzy control technique is introduced to deal with the unknown nonlinear terms containing non-affine fault functions. A new fixed-time fault-tolerant control scheme based on the modified prescribed performance technology is proposed for the NNMASs, which ensures that the closed-loop system satisfies the practical fixed-time stability. In addition, the consensus tracking errors of the NNMASs converge to a given range within a prescribed performance bound in a fixed time. Finally, comparative simulation results show the effectiveness of the proposed method.

CRAFT: Criticality-Aware Fault-Tolerance Enhancement Techniques for Emerging Memories-Based Deep Neural Networks

Deep Neural Networks (DNNs) have emerged as the most effective programming paradigm for computer vision and natural language processing applications. With the rapid development of DNNs, efficient hardware architectures for deploying DNN-based applications on edge devices have been extensively studied. Emerging Non-Volatile Memories (NVMs), with their better scalability, non-volatility and good read performance, are found to be promising candidates for deploying DNNs. However, despite the promise, emerging NVMs often suffer from reliability issues such as stuck-at faults, which decrease the chip yield/memory lifetime and severely impact the accuracy of DNNs. A stuck-at cell can be read but not reprogrammed, thus, stuck-at faults in NVMs may or may not result in errors depending on the data to be stored. By reducing the number of errors caused by stuck-at faults, the reliability of a DNN-based system can be enhanced. This paper proposes CRAFT, i.e., Criticality-Aware Fault-Tolerance Enhancement Techniques to enhance the reliability of NVM-based DNNs in the presence of stuck-at faults. A data block remapping technique is used to reduce the impact of stuck-at faults on DNNs accuracy. Additionally, by performing bit-level criticality analysis on various DNNs, the critical-bit positions in network parameters that can significantly impact the accuracy are identified. Based on this analysis, we propose an encoding method which effectively swaps the critical bit positions with that of non-critical bits when more errors (due to stuck-at faults) are present in the critical bits.

Observer and fault reconstruction-based control design for interval type-2 fuzzy systems

In this study, the finite-time stabilization and multiple faults reconstruction issues are scrutinized for nonlinear control systems in the presence of time-varying state delay, actuator faults and sensor faults in which the underlying system is approximated by dint of fuzzy approach. To be specific, the interval type-2 (IT2) fuzzy technique is deployed in tandem with a non-parallel distribution compensation scheme to linearize such a faulty system. Further, the existence of unknown inputs in both the system and its output are effectively reconstructed with the aid of IT2 fuzzy-based observer, namely the IT2 fuzzy unknown input (UI) observer. Simultaneously, the designed observer also ensures the reconstruction of system state vectors that are not able to be measured. With the help of this reconstruction information, a fault-tolerant control scheme is established to maintain the performance of the addressed system even in the occurrence of unknown faults. Particularly, in this work, the sampling strategy is incorporated with the developed controller design. Overall, the proposed IT2 fuzzy UI observer-based sampled fault-tolerant controller guarantees the stability of the resulting system within a finite-time duration. Furthermore, the Lyapunov stability theory enables to formulate the delay-dependent adequate constraints in the framework of linear matrix inequalities. On the grounds of established criteria, the relations are also constructed for obtaining the controller and observer gain matrices. Eventually, the simulation study is conducted with two numerical examples to validate the theoretical and practical significance of the developed control procedure.

Secure-fault-tolerant efficient industrial internet of healthcare things framework based on digital twin federated fog-cloud networks

The Industrial Internet of Healthcare Things (IIoHT) is the emerging paradigm in digital healthcare. Context-aware healthcare sensors, local intelligent watches, healthcare devices, wireless communication technologies, fog, and cloud computing are all parts of the IIoHT used in healthcare. The ubiquitous healthcare services it provides to its users in practice. However, the current IIoHT healthcare frameworks have security and failure issues in mobile fog and cloud networks where they are spread out. This paper presents the secure, fault-tolerant IIoHT Framework based on digital twin (DT) federated learning-enabled fog-cloud models. The DT is an effective technology that makes virtual copies of servers at different locations. DT integrated with federated learning inside the fog and cloud environments, where the failure of tasks and execution improved for healthcare sensor data. The study aims to reduce processing time and the risk of task failure. The study presents the Secure and Fault-Tolerant Strategies (SFTS)-enabled IIoHT framework that optimizes wearable sensor data and executes it with the minimum offloading and processing delays. Simulation results show that the proposed work minimized the security risk by 40%, failure risk of tasks risk by 50%, and the training and testing time by 39% for sensor data during the execution of mobile fog cloud networks.