Fault-tolerant Scheme Research Articles

Fault Tolerant Control Strategy for Doubly Salient Electromagnetic Machine Based on Current Profile Function Under Loss of Excitation

Fault Tolerant Control Strategy for Doubly Salient Electromagnetic Machine Based on Current Profile Function Under Loss of Excitation

An Adaptive Fault-Tolerant Control Scheme for 6-DOF Spacecraft Based on A Control Gain Re-Construction

An Adaptive Fault-Tolerant Control Scheme for 6-DOF Spacecraft Based on A Control Gain Re-Construction

Neural Dynamic Fault-Tolerant Scheme for Collaborative Motion Planning of Dual-Redundant Robot Manipulators.

To avoid the task failure caused by joint breakdown during the collaborative motion planning of dual-redundant robot manipulators, a neural dynamic fault-tolerant (NDFT) scheme is proposed and applied. To do so, a joint fault-tolerant strategy is first designed, and it is formulated as a time-varying equality constraint. Second, combining the robot position and orientation control, joint limit constraint, joint fault-tolerant equality constraint, and considering the repetitive motion optimization criterion, a fault-tolerant framework for the dual-redundant robot manipulators based on quadratic programming (QP) is constructed. Then, a varying-parameter recurrent neural network (VP-RNN) is designed to solve the QP issue. The fault-tolerant framework and the VP-RNN constitute NDFT scheme. With the NDFT scheme, the impact of faulty joints on the whole system can be remedied by healthy joints, thereby the end-effectors of the robot can complete the given end-effector task. Finally, computer simulations and physical experiments are implemented to verify the availability, physical realizability, and accuracy of the proposed NDFT scheme in the collaborative execution of end-effector tasks. Comparative experimental results with conventional repetitive motion planning schemes based on neural networks show higher accuracy and smaller joint angle drift.

Neural-Network-Based Adaptive Fixed-Time Control for Nonlinear Multiagent Non-Affine Systems.

In this research, the adaptive neural network consensus control problem is addressed for a class of non-affine multiagent systems (MASs) with actuator faults and stochastic disturbances. To overcome difficulties associated with actuator faults and uncertain functions of the designed MAS, a neural network fault-tolerant control scheme is developed. Moreover, an adaptive backstepping controller is developed to solve the non-affine appearance in multiagent stochastic non-affine systems using the mean value theorem. Being different from the existing control methods, the developed adaptive fixed-time control approach can ensure that the outputs of all followers track the reference signal synchronously in the fixed time, and all signals of the controlled system are semi-globally uniformly fixed-time stable. The simulation results confirm that the presented control strategy is effective in achieving control goals.

Fault Tolerant Control of a Dual Star Induction Machine Drive System using Hybrid Fractional Controller

Abstract This paper presents a novel fault tolerant control (FTC) strategy for a dual star induction machine (DSIM) based on the combination of two types of robust controllers, namely a proportional resonant (PR) controller for current regulation and a fractional order PI (FOPI) for speed regulation. This FTC is associated with an indirect rotor indirect rotor field-oriented control (IRFOC) strategy. Fault feedforward compensation of the current components is introduced using the residual signal generated by the calculations passing through the PR controller. The fractional-order PI controller is applied as a feedforward fractional-order perturbation observer to the speed control loop, which attempts to minimise the error induced by the fault. In this context, a fault-tolerant control scheme is achieved. The performance characteristics of the proposed fault tolerant control for a dual star induction machine drive are compared with the fault tolerant control based on the conventional integer order IP (IOPI) to verify the effectiveness of the proposed FTC scheme under various conditions, by examining the robustness of the control in the presence of faults. To evaluate the performance of the proposed technique, simulation results are obtained using the Matlab/Simulink environment. According to the obtained simulation results, the proposed FTC system achieves significantly better responses than the conventional IRFOC system in terms of harmonics in the stator currents, and low oscillations in the electromagnetic torque response.

Drone fault tolerance strategy by allowing remaining rotor to pan and tilt

Drone fault tolerance strategy by allowing remaining rotor to pan and tilt

Drone fault tolerance strategy by allowing remaining rotor to pan and tilt

Drone fault tolerance strategy by allowing remaining rotor to pan and tilt

A Novel Fault-Tolerant Scheme for Multi-Model Ensemble Estimation of Tire Road Friction Coefficient With Missing Measurements

A Novel Fault-Tolerant Scheme for Multi-Model Ensemble Estimation of Tire Road Friction Coefficient With Missing Measurements

Neuro‐Adaptive Finite Time Composite Fault Tolerant Control for Attitude Control Systems of Satellites

AbstractAn adaptive finite time composite fault tolerant control strategy based on an optimized neural network for Attitude control systems (ACSs) of satellites is proposed considering the state time‐varying delays, concurrent actuator and sensor faults, system uncertainties, modelable external disturbance and operating noise. An uncertain time‐varying state space model for ACSs of satellites is established, and sensor faults are equivalent to actuator‐like faults. A disturbance observer is designed for estimating the modelable external disturbance, and an improved dwarf mongoose optimization (DMO) algorithm based on the Levy flight distribution is utilized to optimize the basis function of hyperbasis function neural networks to better estimate the augmented actuator faults that include the actuator fault and the actuator‐like fault. Furthermore, an adaptive finite time composite fault‐tolerant controller is proposed, which includes the delay‐dependent feedback control law, disturbance estimation based‐disturbance compensation law and the adaptive fault compensation law based on the augmented fault estimation using the improved DMO‐hyper basis function neural network. The finite time boundness of the close‐loop dynamics to the uncertainties, operating noise, and augmented actuator faults and the robustness of the measurement to the uncertainties, operating noise and augmented actuator faults are analyzed, and the observer and controller design is formulated as the linear matrix inequalities. Simulation examples for ACSs in different working conditions are considered to exhibit the proposed method's effectiveness.

Global adaptive neural network control of nonlinear time-varying systems with unknown control coefficients and model uncertainties

This paper investigates the adaptive control problem for the strict-feedback nonlinear time-varying system (NTVS) with unknown control coefficients and model uncertainties. To address the problem that the neural network (NN) can only achieve local approximation of unknown nonlinear functions, a novel NN control scheme is proposed. The scheme consists of a NN controller that operates inside the approximation domain and a robust controller that operates outside the approximation domain, and a novel switching function is designed to enable the two controllers to switch smoothly in the vicinity of the approximation domain to ensure that all signals of the closed-loop system are globally uniformly ultimately bounded. To address the problem that unknown time-varying control coefficients are difficult to handle, an adaptive fault-tolerant control scheme is proposed in combination with the congelation of variables method. The scheme employs classical adaptive control to adapt to the variation of unknown control coefficients and robust control to eliminate the time-varying disturbance terms, and introduces a positive integrable time-varying function to achieve asymptotic tracking of an arbitrary reference signal. The combination of these two schemes constitutes a global adaptive neural network control (GANNC) method for the NTVS with unknown control coefficients and model uncertainties. Finally, an adaptive attitude fault-tolerant controller for launch vehicles is designed by using the GANNC method, which can realize the accurate tracking of the attitude control system to the reference command under normal status, strong disturbances and actuator failures, and comparative experiments prove the effectiveness and superiority of the controller.

Self-triggered fixed-time bipartite fault-tolerant consensus for nonlinear multiagent systems with function constraints on states

This article presents a self-triggered bipartite fault-tolerant consensus control scheme in a fixed-time stability sense for multiagent systems (MASs) with function constraints on states. Neural networks are introduced to identify unknown nonlinearities, thereby relaxing constraints on unknown functions. Then, based on time-varying barrier Lyapunov functions including time and previous states, all states of the considered MASs are constrained to given ranges. In addition, to improve the utilization of system transmission resources, a self-triggered control method in which the next triggering time can be computed according to the current information of the controller is proposed. By applying the Lyapunov theory, the designed self-triggered controller can ensure that all signals containing consensus tracking errors are fixed-time bounded under a given communication topology, and the Zeno phenomenon is successfully avoided. Finally, a practical example is provided to testify the feasibility of the developed control scheme.

Tuning function-based fault-tolerant finite-time control for nonlinear systems with sensor/actuator faults

This paper discusses the adaptive fault-tolerant finite-time control tracking problem for a class of strict-feedback nonlinear systems with multiple faults and disturbances. Our essay focuses on the problem of over-parameterisation in traditional backstepping methods. A new fault-tolerant control scheme is proposed by combining tuning function with fuzzy control technology and finite-time control. This scheme greatly simplifies the process of derivation and computation and avoids high computational cost and high memory consumption. Thus the purpose of improving the reliability and robustness of the system is achieved. Finally, according to the Lyapunov finite-time stability theory, the proposed method can ensure that the closed-loop system remains semi-globally practically finite-time stable and the tracking errors converge to a small neighbourhood near the origin in finite time under the condition of unmatched disturbances and sensor/actuator faults simultaneously. The effectiveness of the proposed control strategy is verified by a simulation example.

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.