Fault Estimation Error Research Articles

Flexible spacecraft's active fault-tolerant and anti-unwinding attitude control with vibration suppression

Reliable spacecraft's attitude control is one of the core research areas of the space industry due to the need for sustainable space exploration and advancements in space technology. In this paper, an active fault-tolerant control scheme is designed for a flexible spacecraft's attitude control experiencing multiple actuator faults, inertial uncertainties, external disturbances, and fault estimation errors while suppressing the flexible appendages' vibrations. First, a nonlinear flexible spacecraft's relative attitude model with multiple actuator faults is outlined. A sliding mode-based flexible vibration observer is proposed to estimate the flexible appendage-related vibrations. The designed active fault-tolerant control scheme comprises an adaptive time-varying fault-detection strategy for quickly detecting actuator faults and avoiding false alarm rates caused by uncertainties and disturbances. An extended state observer then estimates the lumped faults effects, including the multiple actuator faults and uncertainties. The driving control is switched from nominal to fault-tolerant once the faults are successfully identified with acceptable accuracy. Besides, based on the information from the fault identification and flexible vibration observer, a novel, unwinding-free, and adaptive fault-tolerant attitude controller is designed to effectively acclimatize the detected faults, despite errors in lumped faults and flexible modes' estimations. With multiple actuator faults and uncertainties, the remunerative and precise attitude tracking control with vibration suppression is realized without using smart vibration-suppression materials. Finally, the efficacy of the propounded scheme is illustrated via comparative simulation outcomes with the existing results.

Observer-based fault estimation in steer-by-wire vehicle

In this paper, the mechanism for the fault estimation (FE) problem for a steer-by-wire (SBW) vehicle with sensor and actuator faults is investigated. To deal with the design issues, we transformed the nonlinear model of SBW vehicle into a new coordinate system to jointly estimate the sensor and actuator faults. In the new coordinate system, the Lipschitz conditions and system uncertainties are also considered. The proposed schemes essentially transform the original system into two subsystems, where subsystem-1 includes the effects of actuator faults but is free from sensor faults and subsystem-2 only has sensor faults. Then two sliding mode observers (SMOs) are designed to estimate actuator and sensor faults, respectively. The sufficient conditions for the existence of the proposed observers with FI°o performance are derived and expressed as an LMI optimization problem such that the upper bounds of the state and fault estimation errors can be minimized. Finally, the numerical example with simulation results is provided to validate the practicability and efficacy of the developed estimation strategy

Design of finite-time active fault tolerant control system with real-time fault estimation for a remotely operated vehicle

In this paper, a new approach is introduced for active fault tolerant control system of an underwater vehicle with unknown dynamics in the presence of thruster system faults and constraints. For this, a finite-time observer is proposed to estimate unknown system dynamics and the faults and failures of the thruster system simultaneously. In the observer, a novel unit based on acceleration feedforward is introduced into conventional extended-state observer so that real-time estimation of fault can be obtained regardless of uncertainties and disturbances. Stability analyses are carried out for the observer and it is proved that system states and uncertainties can be estimated in finite time and fault estimation error is proved to be globally uniformly ultimately bounded. In order to actively accommodate thruster system faults and failures, and to take saturation and rate limits into account, control allocation is utilized. To outline the advantages of the proposed control system, comparisons are made with previous works. The results show that the proposed observer has effectively estimated thruster system faults and failures. It is also shown that the post-failure/-fault behavior of the vehicle has significantly improved and despite keeping the position more accurately, the performance of the thruster system has considerably improved.

Resilient observer design of sensor fault estimation for discrete‐time multi‐agent systems: A distributed approach

AbstractIn this article, the issue of distributed sensor fault estimation with resilient property is studied for a class of multi‐agent systems. Based on the relative output information, a novel fault estimation design is presented to estimate sensor fault and state vectors simultaneously. Meanwhile, the variation of observer gain is considered to improve the robustness of fault estimation observer. Furthermore, robust performance is used to suppress the influence of external interference on sensor fault estimation error. Finally, simulations are provided to present effectiveness of the developed approach.

A 6-DOF sliding mode fault tolerant control solution for in-orbit autonomous rendezvous

The goal pursued by this article, is to evaluate the potential of sliding-mode control and estimation techniques, to address fault tolerance against a large class of actuator faults, including loss of controllability of the faulty actuator, for autonomous rendezvous between a chaser spacecraft and a passive spacecraft on a circular orbit. The proposed solution is based on the dual quaternion formalism, to describe in a single equation, rotational and translational spacecraft dynamics, solar array flexible modes, propellant sloshing, the most dimensioning space disturbances, and their coupling. Such a modelling formalism enables to propose a six degree-of-freedom fault tolerant control architecture, which relies on the generalized super-twisting control algorithm nested with a nonlinear fault estimator. An anti-windup strategy based on polytope algebra is applied to the control algorithm, to prevent instability due to actuator saturation when faults occur. Asymptotic stability of the proposed fault-tolerant control scheme is formally proved with respect to a wide variety of faults, providing that the first derivatives of the fault estimation error versus time and the sliding surface, are bounded. Intensive simulations from a functional engineering simulator that accurately simulates the rendezvous mission, are presented in the paper, as well as capture-oriented criteria. The presented results demonstrate that the proposed fault-tolerant solution is able to cover any kind of thruster faults, including total loss of controllability of the faulty thruster, as well as solar array flexible modes, propellant sloshing, gravity gradient, the second zonal harmonic, atmospheric drag and magnetic disturbances.

Sliding-Mode Observer-Based Fault Reconstruction for T-S Fuzzy Descriptor Systems

This article discusses the problem of sliding-mode observer (SMO)-based fault reconstruction for T-S fuzzy descriptor systems by using the RBF neural network. First, a descriptor learning SMO, without the observability condition of fast subsystem, is developed such that the reconstruction of fault and states is achieved, simultaneously. Then, a sufficient linear matrix inequality (LMI) condition is presented to guarantee the admissibility of the sliding motion. Meanwhile, the LMI condition ensures the boundedness of the state and fault estimation errors. By utilizing robust H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> strategy, the impact of disturbance on the system can be reduced. Moreover, an RBF neural network-based sliding-mode control (SMC) strategy is adopted to estimate the nonlinearity, unknown positive constants, and ensure the reachability condition, simultaneously. Finally, an example is presented to verify the efficacy of our approach.

Fault Diagnosis Algorithm Based on Adjustable Nonlinear PI State Observer and Its Application in UAV Fault Diagnosis

Aiming at the problem of fault diagnosis in continuous time systems, a kind of fault diagnosis algorithm based on adaptive nonlinear proportional integral (PI) observer, which can realize the effective fault identification, is studied in this paper. Firstly, the stability and stability conditions of fault diagnosis method based on the PI observer are analyzed, and the upper bound of the fault estimation error is given. Secondly, the fault diagnosis algorithm based on adjustable nonlinear PI observer is designed and constructed, it is analyzed and we proved that the upper bound of fault estimation under this algorithm is better than that of the traditional method. Finally, the L-1011 unmanned aerial vehicle (UAV) is taken as the experimental object for numerical simulation, and the fault diagnosis method based on adaptive observer factor achieves faster response speed and more accurate fault identification results.

Design and Experimental Development of Wireless Iterative Learning Fault Estimation Algorithm With Quantization and Packet Losses

In this paper, a wireless iterative learning fault estimation algorithm (WILFEA) is proposed and validated experimentally with the aim to achieve perfect tracking of a prescribed reference trajectory for systems with packet losses and quantizer measurements that operate repetitively. First, state variables, Markov chain process of random packet losses, and a logarithmic quantizer are considered to establish an extended-state-space system model. Next, based on this model, sufficient conditions for linear repetitive processes are developed with the Lyapunov-Krasovskii technique and $H_{\infty }$ approach is applied to calculate the observer gain and the learning gain. Then, WILFEA based fault estimation is constructed. Compared with the existing methods, the proposed WILFEA improves the fault estimation performance in the current iteration by consider both state error and fault estimation error. Finally, the simulation and experimental results are used for DC-Servomotor system to illustrate the effectiveness of the proposed approach using Matlab/simulink software, LabVIEW Software, ZigBee Xbee and Arduino board.

Adaptive Fault Estimation for Unmanned Surface Vessels With a Neural Network Observer Approach

This paper is concerned with the fault reconstruction observer design problem with unknown nonlinearities, external disturbances and faults. First, a neural-network-based fault estimation approach is developed to generate the estimations of the actuator failures. In this design, the neural network strategy is utilized to approximate the totally unknown nonlinear functions. Then, an iterative adaptive observer is designed to offer the accurate estimations of the sensor faults, where the estimations in the previous iteration are applied in the current iteration to guarantee the convergence of sensor fault estimation errors. The developed neural network observer design approach can reconstruct the states, actuator and sensor faults for the unmanned surface vessel simultaneously. Finally, a practical example of the unmanned surface vessel is presented to illustrate the effectiveness and the potential of the proposed observer technique.

Sliding mode observer for estimating states and faults of linear time-delay systems with outputs subject to delays

In this paper, we study the problem of estimating states and faults for a class of linear time-delay systems using sliding mode observers. Both the states and outputs are subject to constant delays. This is a challenging problem and has not yet been resolved in the literature. By appropriately using a system transformation and the H∞ filtering method, we propose a novel sliding mode observer scheme which can guarantee H∞ asymptotic stability of the state and fault estimation errors. A numerical example is provided to illustrate the effectiveness of the new result.

Control for Leader–Follower Consensus of Multi-agent Systems with Actuator Faults Using Decentralized Robust Fault-tolerant Control

The leader–follower consensus tracking is one of the important problems in multi-agent systems (MASs) which can be influenced by actuator faults. The main issue of the paper is fault occurrence, and the challenge is to design an appropriate decentralized fault-tolerant control (FTC) based on fault estimations to minimize the effects of actuator fault and maintain the tracking problem. In this paper, the Luenberger detection filter is adopted for the fault detection process and fault estimations are obtained by recursive least-square (RLS) parameter estimation method. The procedure utilized for FTC is fault hiding technique so that using virtual actuator methodology, the effects of faults are partially improved and a robust controller is combined with a virtual actuator to reduce the tracking error. The main contribution of this paper is designing a robust FTC which not only can compensate for the performance degradation, but also can make the system robust against fault estimation error uncertainties. It is also proved that the tracking error of faulty agents converges to zero asymptotically and the consensus tracking is preserved by using the proposed method. The effectiveness of the proposed method is verified through simulations.

Fault Modeling, Estimation, and Fault-Tolerant Steering Logic Design for Single-Gimbal Control Moment Gyro

This brief addresses the single-gimbal control moment gyro (SGCMG) fault modeling, estimation, and tolerant-control steering logic design problem, aiming at enhancing the reliability and safety of spacecraft attitude control systems. The SGCMG is modeled as a two-loop system, including a wheel speed control loop and a gimbal rate control loop. Each loop contains an electrical motor (EM) and its corresponding variable speed drive (VSD), which may suffer from faults. By analyzing and modeling potential faults of the EM-VSD system, the SGCMG fault model is further developed. Then, a local adaptive fault estimator is proposed to reconstruct the total time-varying fault effects of each SGCMG. It is proven that the gimbal angle estimation error and fault estimation error converge to small compact sets containing zero. Moreover, leveraging estimated fault effects, a fault-tolerant steering logic is further developed to allocate the commanded attitude control torque properly such that the gimbal rate constraints are satisfied, and fault effects are compensated. To verify the proposed fault estimator and fault-tolerant steering logic, numerical simulations are carried out on an SGCMG-actuated spacecraft.

Active fault-tolerant control for a quadrotor helicopter against actuator faults and model uncertainties

This paper proposes an active fault-tolerant control strategy for a quadrotor helicopter against actuator faults and model uncertainties while explicitly considering fault estimation errors based on adaptive sliding mode control and recurrent neural networks. Firstly, a novel adaptive sliding mode control is proposed. In virtue of the proposed adaptive schemes, the system tracking performance can be guaranteed in the presence of model uncertainties without stimulating control chattering. Then, due to the fact that model-based fault estimation schemes may fail to correctly estimate fault magnitudes in the presence of model uncertainties, a fault estimation scheme is proposed by designing a parallel bank of recurrent neural networks. With the trained networks, the severity of actuator faults can be precisely estimated. Finally, by synthesizing the proposed fault estimation scheme with the developed adaptive sliding mode control, an active fault-tolerant control mechanism is established. Moreover, the issue of actuator fault estimation error is explicitly considered and compensated by the proposed adaptive sliding mode control. The effectiveness of the proposed active fault-tolerant control strategy is validated through real experiments based on a quadrotor helicopter subject to actuator faults and model uncertainties. Its advantages are demonstrated in comparison with a model-based fault estimator and a conventional adaptive sliding mode control.

Reduced-Order Estimator-Based Efficient Fault-Tolerant Tracking Control Optimization for Constrained LPV Systems

This article focuses on addressing a fault-tolerant tracking control (FTTC) problem for a class of constrained polytopic LPV systems. The dual-mode predictive control method is introduced to design an efficient FTTC strategy such that the performance output of LPV systems can be regulated to track a given reference within state/input constraints. On the whole, the FTTC policy is constructed by the reduced-order estimator, tracking error feedback, double signal compensation, and optimal predictive regulation (PR). In specific, a novel reduced-order estimator is first designed to provide the state and fault information. Then, an unconstrained robust tracking error feedback control is designed to stabilize the LPV systems. Following that, a double signal compensation mechanism is proposed to remove the influences of fault, fault estimation error and other mismatched disturbances. Afterward, an optimal PR policy is further designed to optimize the tracking performance and handle system constraints. Finally, the effectiveness of these proposed results is verified by three case studies.

Performance comparison of representative model-based fault reconstruction algorithms for aircraft sensor fault detection and diagnosis

This article proposes a nonlinear disturbance observer (NDO) based approach for aircraft inertial measurement unit (IMU) fault detection and diagnosis (FDD) by making use of dynamic and kinematic relations of the aircraft. Furthermore, the detailed aircraft IMU FDD design using four representative fault reconstruction algorithms (NDO, sliding mode observer (SMO), iterated optimal two-stage extended Kalman filter (IOTSEKF) and adaptive two-stage extended Kalman filter (ATSEKF)) is presented. More importantly, this paper presents a thorough FDD performance comparison using these four representative methods. Different FDD performance indexes such as fault detection time, minimum detectable faults and fault estimation errors are compared under various situations such as different fault types and noise standard deviations. The advantages, drawbacks and tuning of each method are investigated, which provide useful insights to aircraft sensor FDD.

Fault tolerant tracking control for a class of linear parameter varying systems using reduced-order simultaneous estimator and optimal preview policy

This paper considers the reduced-order estimator based fault tolerant preview tracking control problem for a class of linear parameter varying (LPV) systems. The main research contents are composed of two aspects. First, a scheduling-parameter-dependent reduced-order simultaneous state/fault estimator is designed based on model transformation method, unknown input observer theory, estimator parameterisation, and robustness optimisation. Second, a novel robust fault tolerant preview tracking control policy is constructed by integrating fault signal compensation, state feedback, preview action of reference signals, and integral regulation of tracking errors. Compared with the relevant studies, the merits can be summarised in two aspects. On one hand, the proposed design method of reduced-order estimator can be applied to more general LPV models because it does not depend on the traditional constraint operations, such as decomposition of system output equation, separated estimations of actuator and sensor faults, or derivative of output equation. On the other hand, the optimal preview policy including preview action and integral regulation is introduced to increase the design degree of freedom, which contributes to the optimisation of fault tolerant tracking performance even under influences of fault estimation errors and disturbances. Finally, the effectiveness of the proposed method is verified by three case studies.

Singular Perturbation-Based Fault-Tolerant Control of the Air-Breathing Hypersonic Vehicle

This article studies the fault-tolerant control issue of the air-breathing hypersonic vehicle subject to the actuator fault. Through singular perturbation modeling, the longitudinal dynamics of the vehicle can be transformed into a three-time-scale singularly perturbed system. A new nonlinear diagnostic observer is designed to estimate the actuator fault first. Then a cyclic-small-gain principle-based distributed fault-tolerant control strategy is developed by regarding the singularly perturbed system as an interconnected system with three subsystems. The proposed distributed fault-tolerant controller can achieve the input-to-state stability of the longitudinal motion at the cruising condition with respect to the fault estimation error. The simulation results show the effectiveness of the proposed method.

Fault estimation of linear discrete time-varying systems with multiplicative noise based on finite impulse response filter

This paper aims to construct a finite impulse response (FIR) based fault estimator for a class of linear discrete time-varying systems (LDTV) with multiplicative noise. Drawing support of intensive stochastic analyses and matrix manipulations, a novel performance index is proposed such that the fault estimation error is minimized in stochastic sense. A necessary and sufficient condition is established to guarantee the existence of the FIR-based fault estimator with satisfied estimation accuracy. The optimal gain of the desired fault estimator is calculated in an analytical way by minimizing the aforementioned performance index. Several examples are presented to demonstrate the effectiveness and superiority of the proposed methods.

Sensor fault estimation of networked vehicle suspension system with deny‐of‐service attack

This study is concerned with the sensor fault estimation problem for network-based vehicle suspension system with deny-of-service attack, where a linear robust observer is designed. First of all, the attack behaviour switching is modelled as a Markovian jumping process, and then a sufficient condition based on the Markovian jumping system approach is proposed such that the sensor fault estimation error system is asymptotically stable in the mean-square sense with a prescribed performance level. In this work, the occurring and transition probabilities of the attack are allowed to be partially unknown and uncertain. Finally, a simulation example is presented that validates the effectiveness of design method.

Lower Triangle Factor-Based Fault Estimation and Fault Tolerant Control for Fuzzy Systems

This article focuses on the problems of observer-based fault estimation and dynamic output feedback fault tolerant control (FTC) for a class of nonlinear fuzzy systems with faults. A novel lower triangle factor-based estimation observer (LTFEO) is proposed for the first time. The designed LTFEO can reduce to the traditional robust estimation observer (REO) and is superior to the adaptive estimation observer (AEO) in the relevant literature, furthermore, the designed LTFEO does not contain the output derivatives. The initial estimation accuracy is improved under the designed estimation observer. An iterative estimation algorithm based on the iterative LTFEOs is given, by which, the obtained mean sequence of state and fault estimation errors can converge to zeros (vector) and the proof is given in theoretical. Then, a new dynamic output feedback FTC is designed to stabilize the faulty system, where the output feedback gains can be determined by state feedback gains and the computational difficulty is decreased. Less conservative stability conditions for the estimation error dynamics and the closed-loop system are given in terms of linear matrix inequalities (LMIs). A tunnel diode circuit system is applied to test the effectiveness and merits of the proposed methods.