Fault-tolerant Reconfiguration Research Articles

Fault-tolerant reconfiguration estimation and control of vehicle state under sensor fault

Purpose The purpose of this study proposes a strategy based on vehicle kinematics, dynamics and fusion estimation. The estimation signal of vehicle driving state is crucial for vehicle driving safety and stability control, and the issue of fault-tolerant reconstruction estimation of vehicle driving state under the failure of yaw rate or lateral acceleration sensors is a significant research topic. Design/methodology/approach A strategy based on vehicle kinematics, dynamics and fusion estimation is proposed. To address the issue of inaccurate calculation of tire forces because of sensor failure, a method combining adaptive sliding mode observer, genetic algorithm and particle swarm optimization algorithm is proposed to accurately calculate tire forces, and the Square Root Cubature Kalman Filter algorithm is used to reconstruct the estimation of vehicle driving state under sensor failure. To improve the accuracy of fault-tolerant reconstruction estimation of vehicle driving state, an error-weighted multi-method fusion estimation strategy for vehicle driving state is proposed. A fast terminal sliding mode control algorithm is proposed to control the stability of the fault-tolerant reconstruction estimation signal of vehicle driving state. Findings Simulation results show that the proposed fault-tolerant reconstruction estimation algorithm for vehicle driving state can accurately estimate the actual driving state of the vehicle and stably participate in the vehicle stability control system, achieving fault-tolerant reconstruction estimation and control of vehicle driving state under sensor failure. Originality/value The problem of vehicle motion state estimation under yaw velocity sensor fault or lateral acceleration sensor fault is solved, and fault tolerance control under sensor state is realized.

A Multi-Model Architecture Based on Deep Learning for Longitudinal Available Overload Prediction of Commercial Subsonic Aircraft with Actuator Faults

Assessing the real-time longitudinal available overload onboard under fault conditions offers vital insights for the fault-tolerant reconfiguration and trajectory planning of commercial subsonic aircraft. After actuator failures in a commercial subsonic aircraft, its aerodynamic model undergoes changes. Traditional methods based on analytical models rely on precise aerodynamic models. However, due to the complexities of the flight environment and uncertainties in disturbances, establishing an accurate aerodynamic model after actuator failures is often challenging. Consequently, traditional methods can yield significant errors when evaluating the available overload under actuator faults. To address this, we introduce a multi-model architecture based on deep learning for the longitudinal available overload prediction of a commercial subsonic aircraft with actuator faults. For flight state data under different working conditions and different faults, Spearman correlation coefficient analysis and the gradient boosting decision tree (GBDT) algorithm are used to remove redundant feature parameters, thereby enhancing the training and prediction speed of the model while reducing the risk of overfitting. To meet prediction accuracy and speed demands, we employ the multi-layer perceptron (MLP) deep learning network to fully explore the environmental features, including uncertainties and disturbances, within the flight state, and the mapping relationships between the flight state and the available overload variations. We incorporate the light gradient boosting machine (LightGBM) and the categorical boosting (CatBoost) algorithms to enhance the model’s prediction speed and fuse it with a longitudinal available overload analytical model to elevate the model’s prediction accuracy, thereby achieving the real-time estimation of the commercial subsonic aircraft’s longitudinal available overload with actuator faults. The results demonstrate that the proposed method achieves a higher accuracy than traditional methods, with a relative error of less than 5%.

Fault-Tolerant Reconfiguration Topology and Control Strategy for Symmetric Open-Winding Multiphase Machines

This paper introduces a fault-tolerant reconfiguration topology and control strategy for symmetric open-winding multiphase machines. Bidirectional switching devices are proposed to connect the inverter bridge legs on different ends when the open-circuit fault occurs, and the reconfigured circuit can regenerate the missing voltage phasors due to the faults. Furthermore, to prevent overcurrent problems on the reconfigured bridge legs and maximize the torque output capability of the reconstructed system, winding currents are optimized with zero-sequence component injection during fault-tolerant operation. The experiments implemented on the five-phase system validate that the proposed fault-tolerant scheme is effective and has higher torque output capability compared with the traditional fault-tolerant solutions.

A fault-tolerant reconfiguration system based on pilot switch for grid-connected inverters

The multi-level inverter has been widely applied in high-power applications, especially in grid-connected systems. In addition to some of the benefits provided by the multi-level inverter, it has the disadvantage to be more prone to failure because of the large number of components used. Therefore, fault-tolerant control of multi-level inverters is critical to ensure the stability and reliability of the system. This paper introduces a fault-tolerant reconfiguration system (FTRS) based on pilot switch for grid-connected inverters. FTRS includes the grid-connected inverter using pilot switches and a fault-tolerant modulation algorithm. FTRS aims to reduce the damage of insulated gate bipolar transistor (IGBT) failure of cascaded H-bridge (CHB) inverter and meet such application requirements as grid-connected systems. The grid-connected inverter using pilot switches and its modulation algorithm can complete fault isolation without the redundant backup H-bridge and can make H-bridge reconfiguration after the system receives the fault signal so that the healthy H-bridge will keep working with voltage peak. Reliability evaluation and comparison of the FTRS and other inverters are presented. The validation of the proposed method is confirmed by simulation and experiment results on the five-level grid-connecter inverter.

Real-Time Vision-Based Aircraft Vertical Tail Damage Detection and Parameter Estimation

The need for reliable, fast, and accurate detection of fault is the key to fault-tolerant control reconfiguration of any flight control system. It is known that faults such as structural damage greatly modify the aerodynamic characteristics, often represented by stability and control derivatives. They also result in abrupt deterioration of flight performance and handling quality, which impairs safety. Since no detection technique is as reliable and fast as the Human Visual System (HVS), this paper demonstrates how popular vision algorithms such as edge detection and structural similarity index can be used for real-time detection of vertical tail damage of a fighter aircraft. A novel geometric method is proposed for fault identification using traditional mathematical geometry. Empirical techniques are used for the estimation of stability and control derivatives to generate a mathematical model of damaged aircraft instead of relying on expensive wind tunnel data. Flight mechanics analysis of the data generated confirms the effects of vertical damage in terms of reduction of static and dynamic directional stability and its control effectiveness. The parametrized state matrix thus obtained for the given flight condition thus leads to efficient real-time control reconfiguration of a damaged aircraft for safe recovery. The importance of this paper lies in using the established techniques of image processing for fault detection of damaged aircraft and parametrization for subsequent control reconfiguration, which is a novel application.

Reconfigurable Framework for Environmentally Adaptive Resilience in Hybrid Space Systems

Due to ongoing innovations in both sensor technology and spacecraft autonomy, onboard space processing continues to be outpaced by the escalating computational demands required for next-generation missions. Commercial-off-the-shelf, hybrid system-on-chips, combining fixed-logic CPUs with reconfigurable-logic FPGAs, present numerous architectural advantages that address onboard computing challenges. However, commercial devices are highly susceptible to space radiation and require dependable computing strategies to mitigate radiation-induced single-event effects. Depending upon the mission, the dynamics of the near-Earth space-radiation environment expose spacecraft to radiation fluxes that can vary by several orders of magnitude. By adopting an adaptive approach to dependable computing, spacecraft computers can reconfigure system resources to efficiently accommodate changing environmental conditions to maximize system performance while satisfying availability constraints throughout the mission. In this article, we propose Hybrid, Adaptive, Reconfigurable Fault Tolerance (HARFT), a reconfigurable framework for environmentally adaptive resilience in hybrid space systems. Furthermore, we describe a methodology to model adaptive systems, represented as phased-mission systems using Markov chains, subject to the near-Earth space-radiation environment, using a combination of orbital perturbation, geomagnetic field, and single-event effect rate prediction tools. We apply this methodology to evaluate the HARFT architecture using various static and adaptive strategies for several orbital case studies and demonstrate the achievable performability gains.

Active attitude fault-tolerant tracking control of flexible spacecraft via the Chebyshev neural network

This paper describes a novel finite-time attitude tracking control approach for flexible spacecraft. This is achieved by integrating sliding-mode control and the active real-time fault-tolerant reconfiguration method. In this approach, the attitude error dynamics and the kinematics of the flexible spacecraft are first established. Then, a nonsingular terminal sliding-mode surface is designed, based on finite-time control theory. Applying the Chebyshev neural network, the uncertain dynamics induced by external disturbances and uncertain inertia parameters are approximated and estimated. The nominal control law and the compensation control law to obtain the active reconfiguration fault-tolerant controller are finally developed in normal and fault conditions, respectively. The closed-loop tracking system is proved to be uniformly ultimately bounded stable after a finite time. Numerical simulations are presented for a flexible spacecraft to illustrate the efficiency of the proposed controller.

Data-Driven Reachability Analysis for the Reconfiguration of Vehicle Control Systems

The paper presents a reconfigurable control strategy for the lateral stability of autonomous vehicles. The control strategy is based on the analysis of big data, which are provided by the sensor networks of autonomous vehicles. The core of the analysis method is a machine learning algorithm, with which the impacts of various vehicle signals on the lateral dynamics have been examined. In the analysis several scenarios with faults in the steering and in-wheel systems are considered using a high-fidelity simulation software. The results of the examination are built into the fault-tolerant reconfiguration strategy.

Robust flatness-based switching reconfiguration control using state flow machines of electronic throttle valve

In this paper, a robust Fault Tolerant Control reconfiguration approach using State Flow Machines is proposed. Indeed, this reconfiguration strategy is based on robust flatness-based switching control using state machines and flow charts. This approach is developed in discrete time framework in order to track a reference trajectory starting from a flat output variable. For each model, a corresponding flatness-based controller is designed and consequently, a multi controller structure is obtained. The switching flatness-based control is based on switching between identified Operating Modes (OM) using state flow machines. The Luenberger observer’s gains are determined using LMIs tools in order to identify the corresponding OM. The localization of the current OM is carried out by minimization of a performance test characterizing the distance between the system and the given OM. Study of the stability as well as the use of anti-windup devices related to switching between controllers have been considered in the proposed approach. The proposed approach is applied to the nonlinear system which is in our case of study an Electronic Throttle Valve (ETV) using state flow machines modeling.

Efficient Fault-Tolerant Topology Reconfiguration Using a Maximum Flow Algorithm

With an increasing number of processing elements (PEs) integrated on a single chip, fault-tolerant techniques are critical to ensure the reliability of such complex systems. In current reconfigurable architectures, redundant PEs are utilized for fault tolerance. In the presence of faulty PEs, the physical topologies of various chips may be different, so the concept of virtual topology from network embedding problem has been used to alleviate the burden for the operating systems. With limited hardware resources, how to reconfigure a system into the most effective virtual topology such that the maximum repair rate can be reached presents a significant challenge. In this article, a new approach using a maximum flow (MF) algorithm is proposed for an efficient topology reconfiguration in reconfigurable architectures. In this approach, topology reconfiguration is converted into a network flow problem by constructing a directed graph; the solution is then found by using the MF algorithm. This approach optimizes the use of spare PEs with minimal impacts on area, throughput, and delay, and thus it significantly improves the repair rate of faulty PEs. In addition, it achieves a polynomial reconfiguration time. Experimental results show that compared to previous methods, the MF approach increases the probability to repair faulty PEs by up to 50% using the same redundant resources. Compared to a fault-free system, the throughput only decreases by less than 2.5% and latency increases by less than 4%. To consider various types of PEs in a practical application, a cost factor is introduced into the MF algorithm. An enhanced approach using a minimum-cost MF algorithm is further shown to be efficient in the fault-tolerant reconfiguration of heterogeneous reconfigurable architectures.

Centralised versus Decentralised Control Reconfiguration for Collaborating Underwater Robots

The present paper introduces an approach to fault-tolerant reconfiguration for collaborating underwater robots. Fault-tolerant reconfiguration is obtained using the virtual actuator approach, Steffen (2005). The paper investigates properties of a centralised versus a decentralised implementation and assesses the capabilities under communication constraints between the individual robots. In the centralised case, each robot sends information related to its own status to a unique virtual actuator that computes the necessary reconfiguration. In the decentralised case, each robot is equipped with its own virtual actuator that is able to accommodate both local faults and faults within a collaborating unit. The paper discusses how this is done through exploiting structural information (e.g. thruster configuration) for each participant in the cooperation. A test scenario is presented as a case in which an underwater drill needs to be transported and positioned by three collaborating robots as part of an underwater autonomous operation.

Flexible rerouting schemes for reconfiguration of multiprocessor arrays

In a multiprocessor array, some processing elements (PEs) fail to function normally due to hardware defects or soft faults caused by overheating, overload or occupancy by other running applications. Fault-tolerant reconfiguration reorganizes fault-free PEs to a new regular topology by changing the interconnection among PEs. This paper investigates the problem of constructing as large as possible logical array with short interconnects from a physical array with faults. A flexible rerouting scheme is developed to improve the efficiency of utilizing fault-free PEs. Under the scheme, two efficient reconfiguration algorithms are proposed. The first algorithm is able to generate the maximum logical array (MLA) in linear time. The second algorithm reduces the interconnect length of the MLA, and it is capable of producing nearly optimal logical arrays in comparison to the lower bound of the interconnect length, that is also proposed in this paper. Experimental results validate the efficiency of the flexible rerouting schemes and the proposed algorithms. For 128×128 host arrays with 30% unavailable PEs, the proposed approaches improve existing algorithm up to 44% in terms of logical array size, while reducing the interconnection redundancy by 49.6%. In addition, the proposed algorithms are more scalable than existing approaches. On host arrays with 50% unavailable PEs, our algorithms can produce logical arrays with harvest over 56% while existing approaches fail to construct a feasible logical array.

Stabiliser Fault Emergency Control using Reconfiguration to Preserve Power System Stability

Stabiliser faults in multi-machine power systems are examined in this paper where fault-masking and system reconfiguration of the nonlinear system are obtained using a virtual actuator approach. Phasor Measurement Units, which can be integrated in wide-area transmission grids to improve the performance of power system stabilisers, are utilised when reconfiguring remaining stabilisers after a local failure has made one inoperable. A stability-preserving reconfiguration is designed using absolute stability results for Lure type systems. The calculation of a virtual actuator that relies on the solution of a linear matrix inequality (LMI) is detailed in the paper. Simulation results of a benchmark transmission system show the ability of the fault-tolerant reconfiguration strategy to maintain wide-area stability of a power system despite failure in one of the stabilisers.

Energy-aware fault-tolerant network-on-chips for addressing multiple traffic classes

This paper presents an energy efficient architecture to provide on-demand fault tolerance to multiple traffic classes, running simultaneously on single network on chip (NoC) platform. Today, NoCs host multiple traffic classes with potentially different reliability needs. Providing platform-wide worst-case (maximum) protection to all the classes is neither optimal nor desirable. To reduce the overheads incurred by fault tolerance, various adaptive strategies have been proposed. The proposed techniques rely on individual packet fields and operating conditions to adjust the intensity and hence the overhead of fault tolerance. Presence of multiple traffic classes undermines the effectiveness of these methods. To complement the existing adaptive strategies, we propose on-demand fault tolerance, capable of providing required reliability, while significantly reducing the energy overhead. Our solution relies on a hierarchical agent based control layer and a reconfigurable fault tolerance data path. The control layer identifies the traffic class and directs the packet to the path providing the needed reliability. Simulation results using representative applications (matrix multiplication, FFT, wavefront, and HiperLAN) showed up to 95% decrease in energy consumption compared to traditional worst case methods. Synthesis results have confirmed a negligible additional overhead, for providing on-demand protection (up to 5.3% area), compared to the overall fault tolerance circuitry.

Circuits, Systems, and Technologies for Detecting the Onset of Sudden Cardiac Death Through EKG Analysis

This article discusses the circuits, systems, and technologies that can be put together to design an electronic-vest that is capable of recognizing a cardiac event and alerting emergency services personnel. The discussion includes state-of-the-art in electronic textiles, analog/radio-frequency circuits, flexible antenna design, sensor design, signal processing, and fault tolerant and dynamic reconfiguration of sensors and processing components. Preliminary design, analysis, and prototyping issues are discussed to demonstrate the feasibility of implementing the technologies.

Reconfigurable Fault Tolerance

Commercial SRAM-based, field-programmable gate arrays (FPGAs) have the potential to provide space applications with the necessary performance to meet next-generation mission requirements. However, mitigating an FPGA’s susceptibility to single-event upset (SEU) radiation is challenging. Triple-modular redundancy (TMR) techniques are traditionally used to mitigate radiation effects, but TMR incurs substantial overheads such as increased area and power requirements. In order to reduce these overheads while still providing sufficient radiation mitigation, we propose a reconfigurable fault tolerance (RFT) framework that enables system designers to dynamically adjust a system’s level of redundancy and fault mitigation based on the varying radiation incurred at different orbital positions. This framework includes an adaptive hardware architecture that leverages FPGA reconfigurable techniques to enable significant processing to be performed efficiently and reliably when environmental factors permit. To accurately estimate upset rates, we propose an upset rate modeling tool that captures time-varying radiation effects for arbitrary satellite orbits using a collection of existing, publically available tools and models. We perform fault-injection testing on a prototype RFT platform to validate the RFT architecture and RFT performability models. We combine our RFT hardware architecture and the modeled upset rates using phased-mission Markov modeling to estimate performability gains achievable using our framework for two case-study orbits.

MOF Based Framework for Integration of Software Architecture Analysis Results

运行在网络环境下的软件在自适应过程中,需要集成多种分析方法来进行分析、规划和决策.由于自适应的决策程序或者设计人员以SA(software architecture)模型作为解析和理解分析结果的上下文,这使得分析结果与SA 模型的集成尤为重要.但是,现有的分析方法集成框架多关注于提供输入、执行分析、从而得到分析结果的过程,对分析结果的集成关注不够.针对分析结果与SA 模型集成中元模型、模型和视图3 个层次的挑战,提出一种软件体系结构分析结果集成框架.框架使用MOF(meta-object facility)元建模技术提供ADL(architectural descriptionlanguage)的扩展机制;使用自动生成模型转换实现SA 模型与分析结果的合成;使用代码生成技术扩展建模工具为扩展后的ADL 提供模型视图.最后以3 种分析方法——两种可靠性评估方法和容错风格的规划方法为例,使用集成框架将其加以集成并应用于Ecperf 系统的SA 模型的分析中,从而展示集成框架的可行性和有效性.;In software adaptation, multiple analysis methods are used for analyzing system, planning adaptation strategy, and decision-making. Because the analysis results are interpreted and understood with SA (software architecture) model as context, synthesizing analysis results and the SA model become important. However, current researches on integration of analysis methods do not pay enough attention to the integrating analysis result with the SA model. Considering integration challenges existing in meta-model, model, and view levels, this study proposes an MOF (meta-object facility)-based framework for integration of analysis results. The framework provides the following: An ADL (architectural description language) extension mechanism with support for upward compatibility; automatic generation of model transformation for model synthesis, and code generation to extend modeling tool for view of synthesized model. In the case study, this study uses the framework to integrate three analysis methods for reliability evaluations and fault tolerant reconfiguration, and applies these analysis methods to analyze SA model of J2EE system Ecperf.

Model based fault diagnosis, fault tolerant control and reconfiguration of hydraulic and thermo-fluid processes using analytical redundancy

Analytical redundancy relation (ARR) based approach for fault detection and isolation (FDI) is presented in this article with applications to a hydraulic and a thermo-fluid process. Bond graph modelling, which allows unified representation of multi-energy domain system dynamics, is used to develop model based quantitative FDI schemes. Also, some issues of fault tolerant control and system reconfiguration have been taken up with model simulation. It is shown that ARRs can be systematically derived by exploring causality in bond graph models and fault indicators can be interactively designed. The sensitivity of fault indicators has been validated through simulation.

Minimized Embedding of Arbitrary Hamiltonian Graphs in Fault-tolerant Graph and Reconfiguration at Faults. I. One-fault-tolerant Structures

The paper proposed approaches to minimized embedding of the Hamiltonian graphs in the enveloping fault-tolerant graph representing the structural model of a fault-tolerant multiprocessor computer system. Failures are regarded as faults of vertices and/or connections between the graph vertices. Mathematical studies rely on the group-theoretical analysis of the characteristics of system structure. It underlies the proposed unique approach to designing the one-fault-tolerant and k-fault-tolerant structures retaining after reconfiguration the logical structure of the original target graph and, therefore, the compiled code of system tasks. The minimum fault-tolerant solutions were obtained for one-fault-tolerant and k-fault-tolerant cycles, simple and diagonal grids, and other popular structures, including arbitrary Hamiltonian graphs for which solutions are of minimized nature. Consideration was given to the algorithms of reconfiguration after arbitrary single and multiple faults. Restoration after faults is very simple; it is based on small tables of the group of system automorphisms which enable correct restoration of the system “at the level of theorems” without either static or dynamic additional verification of the reconfiguration process.

Model-based fault-tolerant control reconfiguration for general network topologies

This article describes a fault-tolerant approach to systems with arbitrary network topologies that uses a model-based diagnosis and control reconfiguration mechanism. The authors illustrate this technique using a wireless sensor network as an example.