Fault Estimation Research Articles

Collaborative Design of Observer and Controller for Singular Systems With Transmission Delay Based on Dynamic Event‐Triggered

ABSTRACTThis article investigates the dynamic event‐triggered with communication delay fault estimation and fault‐tolerant control problem of singular systems in network environments under system faults and external disturbances. By considering both the current time and transmission delay output error, a fault diagnosis observer is constructed to estimate the system state and faults simultaneously based on dynamic event‐triggered sampling. On the basis of estimating the system state and fault signals, a fault‐tolerant controller is proposed to compensate for the impact of faults on the system and maintain its performance. Through the collaborative design of observers, controllers, and event‐triggering schemes, the required performance of the faulty system is ensured while reducing the consumption of communication resources. In addition by constructing a new enhanced Lyapunov functional and utilizing linear matrix inequalities and Lyapunov stability theory, the conditions for asymptotic stability of the system are less conservative and easily obtained. Finally, the effectiveness of the proposed method was verified through simulation results of two examples.

Iterative learning based fault estimation for stochastic systems with variable pass lengths and data dropouts

Iterative learning based fault estimation for stochastic systems with variable pass lengths and data dropouts

Integrated fault estimation and control for unknown discrete-time systems: a data-based multivariable-coordinated optimisation method

This paper addresses the integrated fault estimation and robust control problem for unknown linear discrete-time systems. The considered problem is formulated as a multivariable multiobjective optimisation one. A data-based coordinated design strategy that co-designs an output feedback H ∞ / H ∞ controller and a residual generator is proposed to optimise both H ∞ fault estimation and robust control performances. Based on the input and output data, the design parameters of the controller and the residual generator are determined by using Q-learning technique and introducing a new matrix block identification Q-learning method, respectively. Compared with the existing single-variable multiobjective optimisation methods, the proposed strategy can achieve better fault estimation performance, remove the restriction condition of using input and output data, and extend the traditional Q-learning technique to the case of designing a residual generator with a low-rank condition. Finally, simulation results illustrate the effectiveness of the proposed method.

Sampled output data driven convergent fault estimation for continuous nonlinear descriptor systems

Sampled output data driven convergent fault estimation for continuous nonlinear descriptor systems

Design of a novel unknown input-based adaptive fault estimator for nonlinear switched systems and application to liquid-level control system

Design of a novel unknown input-based adaptive fault estimator for nonlinear switched systems and application to liquid-level control system

Fast adaptive unknown input observer for fault and attack estimations in nonlinear systems: an enhanced LMI approach

This paper deals with the problem of robust fault estimation for the Lipschitz nonlinear systems under the influence of sensor faults and actuator faults. In the proposed methodology, a descriptor system is formulated by augmenting sensor fault vectors with the states of the system. A novel fast adaptive unknown input observer (FAUIO) structure is proposed for the simultaneous estimation of both faults and states of a class of nonlinear systems. A new LMI condition is established by utilising the H ∞ criterion to ensure the asymptotic convergence of the estimation error of the developed observer. This derived LMI condition is deduced by incorporating the reformulated Lipschitz property, a new variant of Young inequality, and the well-known linear parameter varying (LPV) approach. It is less conservative than the existing ones in the literature, and its effectiveness is compared with the other proposed approaches. Further, the proposed observer methodology is extended for the disturbance-affected nonlinear system for the purpose of the reconstruction of states and faults/attacks with optimal noise attenuation. Later on, both designed approaches are validated through an application of a single-link robotic arm manipulator in MATLAB Simulink.

System identification and fault reconstruction in solar plants via extended Kalman filter-based training of recurrent neural networks.

This article proposes using the extended Kalman filter (EKF) for recurrent neural network (RNN) training and fault estimation within a parabolic-trough solar plant. The initial step involves employing an RNN to model the system. Given the challenge of fault discernibility in the collectors, parallel EKFs are employed to reconstruct the parameters of the faults. The parameters are used independently to estimate the system output, and the type of fault is isolated based on the estimation errors using another feedforward neural network. To evaluate the effectiveness of the methodology, simulations are conducted on a loop of the ACUREX plant with irradiances from sunny and cloudy days. The results reveal a fault classification accuracy of approximately 90% and a fault reconstruction error below 3%, with even better accuracies in the cloudy dataset than in the sunny dataset.

Investigation on Machine learning based fault detection and estimation in hydro turbines of industrial hydro power plant

Investigation on Machine learning based fault detection and estimation in hydro turbines of industrial hydro power plant

Cooperative-Critic Learning-Based Secure Tracking Control for Unknown Nonlinear Systems With Multisensor Faults.

This article develops a cooperative-critic learning-based secure tracking control (CLSTC) method for unknown nonlinear systems in the presence of multisensor faults. By introducing a low-pass filter, the sensor faults are transformed into "pseudo" actuator faults, and an augmented system that integrates the system state and the filter output is constructed. To reduce design costs, a joint neural network Luenberger observer (NNLO) structure is established by using neural network and input/output data of the system to identify unknown system dynamics and sensor faults online. To achieve the optimal secure tracking control, an augmented tracking system is formed by integrating the dynamics of tracking error, reference trajectory, and filter output. Then, a novel cost function is designed for the augmented tracking system, which employs the fault estimation and the discount factor. The Hamilton-Jacobi-Bellman equation is solved to obtain the CLSTC strategy through an adaptive critic structure with cooperative tuning laws. Besides, the Lyapunov stability theorem is utilized to prove that all signals of the closed-loop system converge to a small neighborhood of the equilibrium point. Simulation results demonstrate that the proposed control method has good fault tolerance performance and is suitable for solving secure control problems of nonlinear systems with various sensor faults.

Reduced-order observer based fault estimation and dynamic output feedback fault-tolerant control for switched fuzzy stochastic systems

Reduced-order observer based fault estimation and dynamic output feedback fault-tolerant control for switched fuzzy stochastic systems

Design of switched unknown input fault estimator with a combination of H∞ performance and adaptive law for faulty Lipschitz switched systems

Design of switched unknown input fault estimator with a combination of H∞ performance and adaptive law for faulty Lipschitz switched systems

An integrated sensor fault estimation and fault tolerant control design approach for continuous-time switched systems

An integrated sensor fault estimation and fault tolerant control design approach for continuous-time switched systems

Adaptive proportional integral derivative fault tolerant control for continuous-time switched systems: A highly maneuverable aircraft technology (HiMAT) vehicle application

This article presents an active fault-tolerant tracking control (FTTC) scheme for switched linear systems with unknown external disturbances and actuator faults. First, a switched adaptive observer (SAO) is designed to simultaneously achieve system state and actuator fault estimation. Second, an active fault-tolerant tracking controller with a reconfigurable adaptive proportional–integral–derivative architecture is proposed based on the results of these estimations. This FTTC technique aims to ensure the tracking of a predefined reference trajectory while also compensating for actuator fault effects. In terms of linear matrix inequalities, less conservatism design conditions for the SAO and fault-tolerant tracking controller are obtained by utilizing the average dwell time technique and multiple Lyapunov functions. Finally, an application to the highly maneuverable aircraft technology vehicle is used to prove the benefits of the presented scheme.

Fault tolerance strategy of general nonlinear systems with actuator and sensor fault based on T‐S fuzzy model

AbstractIn this paper, a fault estimation (FE) and sliding mode fault tolerance strategy (SMFTS) scheme based on T‐S fuzzy model are proposed for a class of general nonlinear systems (GNS) that encounter actuator and sensor faults and disturbance. A coordinate transformation is introduced to convert sensor fault into actuator fault. Then, a fuzzy perturbation estimator is established to estimate the detailed fault and disturbance information. Then, a SMFTS scheme is proposed to minimize the damage caused by the faults. Finally, the simulation results show that the designed algorithm can correctly estimate the fault and ensure the system performance after the fault.

Data-driven model for marine engine fault diagnosis using in-cylinder pressure signals

Effective diagnosis of marine engines that is crucial for safe and reliable ship operations requires fidel tools for the identification of critical faults. However, the unavailability of extensive measured data-sets corresponding to engine faulty conditions renders the development of such tools challenging. This study aims to develop a data-driven fault estimation model considering information extraction methods and regression techniques of low computational effort, namely multiple linear and polynomial regression. The required data-sets for healthy and faulty conditions for four critical faults and their combinations are generated by employing a calibrated zero-dimensional thermodynamic model representing a marine four stroke medium speed diesel engine, which is validated against engine shop trial measurements. Fourier analysis of the derived in-cylinder pressure profiles is employed to calculate the coefficients of the harmonic orders. Several harmonics coefficients sets are used as input to the regression models to estimate the severity of the four considered faults. The results demonstrate that initial 20 harmonics are sufficient to effectively estimate the severity for each fault, whereas polynomial regression is highly effective, exhibiting R 2 values greater than 98 % . This study provides insights on the data-driven simultaneous faults severity estimation, and as such it impacts the advancement of cost-effective diagnostic methods for marine engines.

Markov multi-fault tolerance control of intelligent suspension in blind scenes

This paper focuses on insufficient perception in blind scenes and the failures of the actuator and sensor of vehicle suspension. The safe speed is designed and the fault-tolerant strategy based on the observer is designed when the actuator and sensor fail simultaneously. A speed planning strategy to ensure that vehicles operate at a safe speed is proposed. The Markov suspension fault jump model considering the suspension sensor and actuator faults is established. Based on the fault estimation obtained by the fault observer, a fault tolerance control strategy of sensor signal reconstruction and actuator fault compensation is adopted. The measurement index for evaluating fault tolerance effectiveness has been established, and an exploration of fault tolerance control is conducted across various fault levels, to identify the preferred fault tolerance strategies corresponding to different levels of fault tolerance. The simulation results show that the designed speed control strategy can improve vehicle safety by decelerating in advance, the fault observer is capable of accurately estimating the true values of actuator and sensor faults. The simulation and vehicle test results demonstrate that similar suspension performance has been obtained by fault-tolerant control strategy and preferred fault tolerance under different fault tolerance levels compared with normal fault tolerance.

Adaptive Control of Constrained Nonlinear Systems With Intermittent Faults: A Fixed‐Time Fault‐Tolerant Framework Based on Projection Operator

ABSTRACTThis article investigates the problem of adaptive fixed‐time fault‐tolerant control for nonlinear systems with time‐varying intermittent faults and asymmetric output constraints. For this purpose, a novel adaptive law based on the projection operator technique is developed to estimate time‐varying intermittent faults, which eliminates the problem that the estimated value increases continuously with the accumulation of fault times. Considering that the fault estimation time needs to be less than the minimum time interval of intermittent faults, the fixed‐time design methodology is introduced to ensure that the relationship between the above two can be quantitatively analyzed. Then, a novel adaptive fixed‐time fault‐tolerant control strategy is put forward for the closed‐loop system, in which a universal barrier function is adopted to remain the system output in the time‐varying asymmetric constraint condition invariably. The theoretical analysis indicates that the developed strategy can guarantee the boundedness of all signals in the closed‐loop system in a fixed‐time. Finally, two examples, one of which involves the rotary steerable drilling tool systems are provided to demonstrate the feasibility and validity of the put forward scheme.

Severity Estimation of Inter-Turn Short-Circuit Fault in PMSM for Agricultural Machinery Using Bayesian Optimization and Enhanced Convolutional Neural Network Architecture

The permanent magnet synchronous motor (PMSM) is a key power component in agricultural machinery. The harsh and variable working environments encountered during the operation of agricultural machinery pose significant challenges to the safe operation of PMSMs. Early diagnosis of inter-turn short-circuit (ITSC) faults is crucial for improving the safety of the motor. In this study, a fault diagnosis method based on an improved convolutional neural network (CNN) architecture is proposed, featuring two main contributions. First, a dilated convolutional neural network is combined with residual structures, multi-scale structures, and channel attention mechanisms to enhance the training efficiency of the model and the quality of feature extraction. Second, Bayesian optimization algorithms are applied for the automatic tuning of architecture hyperparameters in deep learning models, achieving automatic optimization of the hyperparameters for the fault diagnosis model of ITSCs. To validate the effectiveness of the proposed algorithm, 17 simulated tests of ITSC fault severities were conducted under both constant conditions and dynamic conditions. The results show that the proposed model achieves the best performance regarding the validation accuracy (98.2%), standard deviation, F1 scores, and feature learning capability compared to four other models with different architectures, demonstrating the effectiveness and superiority of the algorithm.

Prescribed‐time event‐triggered formation control of heterogeneous multi‐agent system under actuator faults and external disturbances

AbstractIn this article, the problem of the prescribed‐time formation control for the heterogeneous multi‐agent systems (MASs) under actuator faults and external disturbances with sampling‐data settings is discussed. A sufficient condition for the MASs to globally converge to a bounded neighborhood within a prescribed time is given, which can ensure the formation control performance of the MASs with actuator faults. Further, an event‐triggered communication mechanism based on the sampled data is designed to reduce the communication burden. Such a triggering mechanism allows for adjusting the triggering interval to a certain extent while ensuring that the Zeno phenomenon is excluded for each agent. To mitigate the impact of the actuator faults and external disturbances on the system formation control, an actuator fault estimator is designed along with a prescribed‐time state observer to estimate the state of each agent. Then an adaptive control strategy is developed so that the MASs can achieve the desired formation within a prescribed time under the actuator faults and external disturbances. Simulation results are performed to validate the effectiveness of the proposed control strategy.

Finite-horizon approximate optimal attitude control based on adaptive dynamic programming for ultra-low-orbit satellite

This study investigates the finite-horizon approximate optimal attitude control problem for ultra-low-orbit satellites, addressing the complexities introduced by substantial disturbance, actuator faults, actuator saturation, and time constraint. Initially, a fixed-time concurrent learning fault and disturbance estimation approach is proposed that relieves persistent excitation constraints and isolates different influences individually. Subsequently, the cost function is designed with actuator fault estimation, ensuring that the control strategy consistently adheres to actuator saturation constraints and can compensate for current faults. Furthermore, based on the adaptive dynamic programming, an approximate optimal attitude control approach is proposed, which employs time-varying activation functions to approximate the optimal cost function. A fixed-time neural network weight adaptation strategy is designed to ensure the precision and reliability of the approximation. Finally, the numerical simulation confirms the validity and practical applicability of the proposed approach in satellite attitude control systems.