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This article addresses the distributed multiple fault isolation, modeling, and the closed-loop fault estimation under asynchronous switching for high speed train (HST) with switched dynamics, which is composed of traction, coasting, and braking. First, directed-graph-quantum-learning-based multiple-agent system (MAS) classifiers are introduced to characterize the joints effects of multiple faults. Some sufficient conditions are derived under the condition that the multiple fault topology contains a directed spanning tree and cycle edge, and these conditions guarantee that the multiple fault isolation problem can be solved under randomized learning techniques. Then, single-integrator agents are employed to capture the time-varying topology of multiple fault modeling, in which edge agreement and persistence condition are used to guarantee asymptotic consensus. After that, a novel robust fault estimation design along with the switched Lyapunov function and average dwell time is proposed for the possible power actuator faults subject to asynchronous switching and electromagnetic interferences. In addition, switched estimators are designed such that the closed-loop system is asymptotically stable. A multiple fault isolation and estimation case is investigated to validate the application of this methodology.

As a key field in music information retrieval, music emotion recognition is indeed a challenging task. To enhance the accuracy of music emotion classification and recognition, this paper uses the idea of inception structure to use different receptive fields to extract features of different dimensions and perform compression, expansion, and recompression operations to mine more effective features and connect the timing signals in the residual network to the GRU module to extract timing features. A one-dimensional (1D) residual Convolutional Neural Network (CNN) with an improved Inception module and Gate Recurrent Unit (GRU) was presented and tested on the Soundtrack dataset. Fast Fourier Transform (FFT) was used to process the samples experimentally and determine their spectral characteristics. Compared with the shallow learning methods such as support vector machine and random forest and the deep learning method based on Visual Geometry Group (VGG) CNN proposed by Sarkar et al., the proposed deep learning method of the 1D CNN with the Inception-GRU residual structure demonstrated better performance in music emotion recognition and classification tasks, achieving an accuracy of 84%.

Due to the time-varying characteristics and the interacted nature of multiple faults in the high-speed train (HST), the fault modeling, isolation, and severity estimation cannot be described accurately using a single model, which may result in poor performance of the conventional fault diagnosis methods. This article introduces the idea of multiple models and second-level adaptation techniques to diagnose multiple faults of the HST traction motor. First, a reduced model description for the multiple faults is given. Then, a multiple fault isolation framework is developed to simplify the time-varying fault parameters space segmentation. Based on the decoupled fault set, a fault estimation scheme with second-level adaptation is used to provide a reliable alarm priority for different fault scenarios. A case study is performed to verify the effectiveness of the proposed approach.

Based on the adaptive control technique, this study investigates the active-passive hybrid fault-tolerant control (FTC) for hypersonic flight vehicles (HFVs) with compound faults, including system and actuator faults. System and actuator compound faults are defined in multi-augmented Takagi-Sugeno fuzzy HFV systems. Indirect passive compensation factors shield the system faults. An observer with adaptive learning rates that combine fault magnitude and fuzzy premise variable is then designed to estimate actuator faults, where a novel bionic variable parameter algorithm improves the sensitivity and accuracy of fault estimation to the incipient fault deviations. Hence the fault-tolerant controller actively compensates for the actuator faults by using estimated information while shielding the system faults. Finally, the improved adaptive active-passive hybrid FTC for compound faults are completed, and the system’s robust stability is proven. Simulation results are provided to illustrate the effectiveness of the proposed FTC scheme.

This article investigates fault detection, isolation and fault-tolerant control for an over-actuated system in hypersonic re-entry vehicle with concurrent actuator faults and disturbance. A series of residuals developed by spatial projection operators is only sensitive to certain faults that can decouple the effects of faults in different directions. Threshold intervals designed through sliding-time windows and a hypothesis test are used to detect faults. Single and concurrent fault isolation can be achieved by utilizing different residual combinations. Subsequently, an augmented observer is introduced to estimate the faults and satisfies the [Formula: see text]-gain constraint to reduce the effect of disturbances. Finally, an adaptive backstepping fault-tolerant control algorithm is designed to achieve stable attitude tracking. The stability of the proposed schemes is proved by Lyapunov and linear matrix inequality theories. Numerical simulation results demonstrate the effectiveness of the proposed methods.

To enhance the steady-state performance of the virtual-vector-based model predictive current control (VV-MPCC) for a five-phase flux-switching permanent magnet motor, an improved multi-vector-based MPCC (MV-MPCC) method with geometric solution is proposed and investigated in this article. The desired voltage is employed to preselect voltage vector candidates and provide a guidance for the geometric division of a sector. According to the link between the reference voltage and the division, the optimal voltage vectors are determined and their duty ratios are obtained by projection method. Compared to the conventional MV-MPCC method using derivative method, the proposed geometric MV-MPCC method employs the desired voltage to quickly determine the voltage vectors and the corresponding duty ratio, which not only alleviates computational burden but also favors for more intuitive implementation. Finally, the effectiveness of the proposed method is verified by experiments.

© 1982-2012 IEEE. This article investigates an enhanced model predictive torque control (MPTC) strategy to improve the fault-tolerant capability of a five-phase permanent magnet synchronous motor drive under open-circuit fault operation. The conventional fault-tolerant MPTC is focused on the control of the stator flux amplitude and the torque in the fundamental subspace, suffering from the influences of the unregulated harmonic subspace and the large computational burden. Hence, an MPTC strategy considering the control of both fundamental and harmonic subspaces is proposed. Apart from the stator flux and the torque, the harmonic current in the x-y subspace is also selected and controlled in this strategy. In addition, to mitigate the workload of the proposed MPTC, the voltage vectors are preselected according to the stator flux error. With the proposed MPTC, the regulation of both subspaces is realized. Furthermore, the computational burden is significantly alleviated. Finally, comparative experiments are conducted to validate the effectiveness of the proposed control strategy.

This study presents a sensor cascading fault estimation and fault-tolerant control (FTC) for a nonlinear Takagi-Sugeno fuzzy model of hypersonic flight vehicles. Sensor cascading faults indicate the occurrence of source fault will cause another fault and the interval between them is really short, which makes it difficult to handle them in succession. A novel multidimensional generalized observer is used to estimate faults by integrating constant offset and time-varying gain faults. Then, a fault-tolerant controller is used to solve system nonlinearity and sensor fault problems. The observer and controller satisfy the performance index and are robust to external disturbances. A sufficient condition for the existence of observer and controller is derived on the basis of Lyapunov theory. Simulation results indicate the effectiveness of the proposed fault estimation and FTC scheme.

This paper develops a closed-loop coupled fault estimation and a multivariable resilient control for high-speed train traction rectifier with upset conditions. By treating the multidimensional adverse states as changes of a single faults evolution factor, we build a reduced dimensional MIMO evolution model satisfying persistently exciting condition. In the presence of coupled faults, a nominal feed-forward controller guarantees the reliability of the closed-loop stability. Then, a fast proportional integral fault estimator is employed to predict the system resilience within a few microseconds. Under system uncertainties, a fault tolerant control scheme, including a unified robust adaptive controller, is designed to enhance the reliability. Applying the conditions of the globally asymptotically stability, the convergence of the resilient system is rigorously proved. A case study is performed to verify the effectiveness of the proposed approach.

Summary This study proposes an improved adaptive fault estimation and accommodation algorithm for a hypersonic flight vehicle that uses an interval type-2 Takagi-Sugeno fuzzy model and a quantum switching module. First, an interval type-2 Takagi-Sugeno fuzzy model for the hypersonic flight vehicle system with elevator faults is developed to process the nonlinearity and parameter uncertainties. An improved adaptive fault estimation algorithm is then constructed by adding an adjustable parameter. The quantum switching module is also applied to the estimation part to select an appropriate algorithm in different fault cases. The estimation results from the given fuzzy observer are used to design a type-2 fuzzy fault accommodation controller to stabilize the fuzzy system. The stability of the proposed scheme is analyzed using the Lyapunov stability theory. Finally, the validity and availability of the method are verified by a series of comparisons on numerical simulation results.

This study proposes a novel nonlinear resilient trajectory control for a quadrotor unmanned aerial vehicle (UAV) using backstepping control and nonlinear disturbance observer. First, a nonlinear dynamic model for the quadrotor UAV that considers external disturbances from wind model uncertainties is developed. A nonlinear disturbance observer is then constructed separately from the controller to estimate the external disturbances and compensate for the negative effects of the disturbances. Based on the estimates from the given observer, a nominal nonlinear backstepping trajectory-tracking position controller is designed to stabilize the subsystems step by step until the ultimate control law is obtained. An extra term is added to the nominal controller to address the problem of actuator effectiveness loss and to ensure system resilience. The stability of the resilient controller is analyzed using Lyapunov stability theory. Simulation results are presented to demonstrate the effectiveness and robustness of the proposed nonlinear resilient controller.

© 1972-2012 IEEE. To improve the fault-tolerant performance of a five-phase flux-switching permanent magnet (FSPM) motor drive under open-circuit fault (OCF) condition, a model predictive current control (MPCC) with pre-selective method and duty ratio control (DRC) technology is proposed and investigated in this paper. First, on the principle of minimizing harmonic voltages in x-y subspace, two zero switching states and the switching state, which generates a larger voltage vector in α-β subspace are pre-selected. Second, voltage vector references in α-β subspace and x-y subspace are predicted to further select active voltage vector candidates. Consequently, the number of current predictions has been significantly reduced, resulting in the alleviation of the computational complexity and the increase of sampling frequency. Third, the DRC approach is applied in conjunction with the pre-selection-based MPCC to improve the steady-state performance. Finally, the effectiveness of the proposed MPCC method for the OCF tolerant five-phase FSPM motor drive is validated by comparative experiments.

This paper proposes a design method of robust fault reconstruction for a 3-DOF helicopter with actuator faults and disturbance. The model of the 3-DOF helicopter considered in this paper is an affine nonlinear system. An adaptive sliding mode observer is firstly proposed such that the actuator fault can be reconstructed through the adaptive law online. Then, based on linear matrix inequalities LMI, a feasible algorithm is explored to solve the designed parameters. Finally, a simulation study of the 3-DOF helicopter is presented to show the effectiveness of the scheme.

This study proposes a solution to the problem of designing adaptive global sliding mode controllers for a class of linear helicopter systems with actuator faults and time delay. An adaptive global sliding mode control approach is proposed based on dynamic nonlinear sliding mode function and adaptive law. The advantages of the controller include elimination of the reaching movement of traditional sliding mode control, realization of online identification of the fault value, and overcoming of the effect of the actuator faults and time delay. In addition, quantum information technique is used to increase the control accuracy of helicopter. Simulation results demonstrate the efficiency and superiority of the proposed method.

A reconfiguration control scheme is proposed for a quadrotor helicopter with loss of control effectiveness via simple adaptive control and quantum logic. Some good control performances of the helicopter can be achieved by applying quadratic optimal control with scheduled stability degree when the system is in normal operation. A parallel feedforward compensator is introduced to fulfill the condition of positive realness, and thus, the simple adaptive controller can be utilized for the helicopter. Quantum logic is also applied to the quadrotor control system, which achieves the quantization of the disturbances patterns. A mathematical model of the quadrotor helicopter for application is obtained from its dynamic equations with pitch, roll, and yaw axes, which includes the parameter perturbation and outer disturbances. Several simulation and experimental results for the faulty helicopter validate the robustness and reconfiguration control capability of the proposed scheme.

In this paper, an improved nonlinear model for a 3-DOF helicopter and its self-repairing control scheme are investigated via quantum information technique. Firstly, adopting the mechanism analysis, a modified dynamic model is developed, in which the couplings among axes are considered. Such a modeling scheme is useful for applications when the accuracy of original model can not be satisfied. Then, a reconfigurable control scheme is designed for the twin rotor helicopter with multiple faults and parametric uncertainties, which combines active disturbance rejection control method with model reference adaptive control method. In addition, quantum information technique is used to increase the accuracy of self-repairing control of helicopter. Finally, simulation verification is presented in both aspects of modeling and control. The effectiveness and feasibility of the proposed scheme are verified by comparative simulations.

Based on the nonlinear equations of a quadrotor, position tracking with unknown parameters and fault-tolerant control scheme is proposed in this article. First, a multi-loop structure is designed with multiple time-scale analysis to make the system realizable. Then, to achieve the goal of accurate tracking, a sliding mode fault observer is added into each loop to accommodate the effects of all the uncertainties. This controller shows great applicability because of the simple structure. The only information the controller needs to know is the limits of uncertainties. In addition, since the above controller cannot guarantee robustness when the system model is unknown, an online adaptive estimation method is proposed to learn the parameter information. Stability of the overall closed-loop system is analyzed with the Lyapunov stability theory, and output tracks the expected trajectory. The simulation is presented with several situations (including adaptive estimation result test/robust test under parameter uncertainties and high-frequency noise/fault-tolerant ability under actuator faults) to verify the effectiveness and feasibility of the proposed scheme.

A signal control strategy based on model reference adaptive control is investigated with a new hybrid petri net model for urban intersection in artery. The hybrid petri net intersection model is simplified via the token transition. The new model can be more in touch with facts through the waiting zones and addition phases. The hybrid petri net is innovatively used as the reference model in the model reference adaptive control method. The predicted traffic flow is precisely computed by the support vector machine. Finally, a series of simulation results with the practical data verify the feasibility of the novel arterial petri net model and the effectiveness of the control strategy.

In this paper, a new direct self-repairing control scheme is developed for a helicopter flight control system with unknown actuator faults and external disturbance. The design of multi-model-based adaptive control is used to accommodate the faulty system under different fault conditions. By appropriate switching based on quantum information technique, the system can be converted to the best model and the corresponding controller. Asymptotic model following performance and system stability is guaranteed. A disturbance observer is introduced to observe the disturbance of the system, which can produce corresponding control signals according to the disturbance. The results including a numerical simulation and a semi-physical verification demonstrate the effectiveness of the proposed self-repairing control approach for the helicopter flight control system.

A multiple model–based fault detection and diagnosis approach for a helicopter with actuator faults and disturbances is developed in this article. Particularly, the actuator is locked in-place and does not respond to subsequent commands. A multiple model scheme is proposed to detect and isolate the faults. In the multiple model scheme, a series of parallel observers are constructed, each of which is based on a model that describes the system in the presence of a particular actuator fault. In addition, quantum information technique is used to choose which model matches the current system. Furthermore, to estimate the locked position, a robust fast adaptive fault estimation algorithm based on linear matrix inequality technique is proposed by considering the disturbances. Finally, the simulation results show the efficiency of this scheme.

This study gives the mathematic model of a quadrotor unmanned aerial vehicle (UAV) and then proposes a robust nonlinear controller which combines the sliding-mode control technique and the backstepping control technique. To achieve Cartesian position trajectory tracking capability, the construction of the controller can be divided into two stages: a regular SMC controller for attitude subsystem (inner loop) is first developed to guarantee fast convergence rapidity of Euler angles and the backstepping technique is applied to the position loop until desired attitudes are obtained and then the ultimate control laws. The stability of the closed-loop system is guaranteed by stabilizing each of the subsystems step by step and the robustness of the controller against model uncertainty and external disturbances is investigated. In addition, an adaptive observer-based fault estimation scheme is also considered for taking off mode. Simulations are conducted to demonstrate the effectiveness of the designed robust nonlinear controller and the fault estimation scheme.