A Neural Network Adaptive Fault-Tolerant Control Method for Launch Vehicles with the Limited Faults

To tolerate the limited faults such as thrust decline or actuator jamming of launch vehicles, an adaptive fault-tolerant control method based on radial basis function neural network (RBFNN) is proposed in this paper. The method is based on a limited faults dynamics model, and the baseline controller is designed based on the pole placement, using RBFNN to online identify and compensate the fault parameters and uncertain disturbances in the model. Then an adaptive fault-tolerant control law is designed based on Lyapunov theory. The simulation results show that the proposed adaptive control method can effectively ensure the attitude stability as well as control accuracy under the limited faults of launch vehicles, compared with the traditional PD control method.

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Study on Sensor with Mechanical Properties in Nuclear Power Plant with Application of BP Neural Network to Fault Tolerant Control

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.644.56 ◽

2013 ◽

Vol 644 ◽

pp. 56-59

Author(s):

Jin Yang Li ◽

Hong Xia ◽

Shou Yu Cheng

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Power Plant ◽

Nuclear Power Plant ◽

Bp Neural Network ◽

Nuclear Power ◽

Control Method ◽

Fault Tolerant ◽

Fault Tolerant Control ◽

Level Sensor

All kinds of sensor with mechanical properties often can go wrong in nuclear power plant. In this kind of situation, it puts forward a kind of active fault tolerant control method based on the improved BP neural network. Firstly, the method will train sensor by BP neural network. Secondly, it will be established dynamic model bank in all kinds of running state. The system will be detected by using BP neural network real time. When the sensor goes wrong, it will be controled by reconstruction. Taking pressurizer water-level sensor as the case, a simulation experiment was performed on the nuclear power plant simulator. The results showed that the proposed method is valid for the fault tolerant control of sensor in nuclear power plant.

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Robust Adaptive Fault-Tolerant Control of Stochastic Systems with Modeling Uncertainties and Actuator Failures

Abstract and Applied Analysis ◽

10.1155/2014/450521 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Wenchuan Cai ◽

Lingling Fan ◽

Yongduan Song

Keyword(s):

Stochastic Systems ◽

Control Method ◽

Fault Tolerant ◽

Negative Impact ◽

Fault Tolerant Control ◽

Fault Detection And Diagnosis ◽

Lyapunov Theory ◽

Actuator Failures ◽

Modeling Uncertainties ◽

Robust Adaptive

This paper deals with the problem of fault-tolerant control (FTC) of uncertain stochastic systems subject to modeling uncertainties and actuator failures. A robust adaptive fault-tolerant controller design method based on stochastic Lyapunov theory is developed to accommodate the negative impact on system performance arising from uncertain system parameters and external disturbances as well as actuation faults. There is no need for on-line fault detection and diagnosis (FDD) unit in the proposed FTC scheme, which not only simplifies the design process but also makes the implementation inexpensive. Numerical examples are provided to validate and illustrate the benefits of the proposed control method.

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Neural network adaptive backstepping fault tolerant control for unmanned airships with multi-vectored thrusters

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410020976611 ◽

2020 ◽

pp. 095441002097661

Author(s):

Shiqian Liu ◽

James F Whidborne

Keyword(s):

Neural Network ◽

Trajectory Tracking ◽

Actuator Saturation ◽

Fault Tolerant ◽

Fault Tolerant Control ◽

Lyapunov Theory ◽

Actuator Faults ◽

Adaptive Backstepping ◽

Parameter Uncertainties ◽

Tracking Errors

This paper presents the fault tolerant control (FTC) of an unmanned airship with multiple vectored thrusters in the presence of model parameter uncertainties and unknown wind disturbances. A fault tolerant control based on constrained adaptive backstepping (CAB) approach, combined with a radial basis function neural network (RBFNN) approximation, is proposed for the airship with thruster faults. A wind observer is designed to estimate the bounded wind disturbances. An adaptive fault estimator is proposed to estimate the unknown actuator faults. A weighted pseudo inverse based control allocation is incorporated to reconstruct and optimize the practical control inputs of the failed airship under constraints of actuator saturation. Rigorous stability analysis shows that trajectory tracking errors of the airship position and attitude converge to the desired set through Lyapunov theory. Numerical simulations demonstrate the fault tolerant trajectory tracking capability of the proposed NN-CAB controller under the actuator faults, even in the presence of aerodynamic coefficient uncertainties, and unknown wind disturbances.

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Fault-tolerant Control of Quadcopter UAVs Using Robust Adaptive Sliding Mode Approach

Energies ◽

10.3390/en12010095 ◽

2018 ◽

Vol 12 (1) ◽

pp. 95 ◽

Cited By ~ 18

Author(s):

Ngoc Phi Nguyen ◽

Sung Kyung Hong

Keyword(s):

Control Method ◽

Fault Tolerant ◽

Sliding Mode ◽

Fault Tolerant Control ◽

Lyapunov Theory ◽

Adaptive Sliding Mode Control ◽

Actuator Faults ◽

Adaptive Sliding Mode ◽

System Uncertainties ◽

Robust Adaptive

In this paper, a fault-tolerant control method is proposed for quadcopter unmanned aerial vehicles (UAV) to account for system uncertainties and actuator faults. A mathematical model of the quadcopter UAV is first introduced when faults occur in actuators. A normal adaptive sliding mode control (NASMC) approach is proposed as a baseline controller to handle the chattering problem and system uncertainties, which does not require information of the upper bound. To improve the performance of the NASMC scheme, radial basis function neural networks are combined with an adaptive scheme to make a quick compensation in presence of system uncertainties and actuator faults. The Lyapunov theory is applied to verify the stability of the proposed methods. The effectiveness of modified ASMC algorithm is compared with that of NASMC using numerical examples under different faulty conditions.

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Fault-tolerant control for multiple networked spacecraft under actuator saturation

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410016640824 ◽

2016 ◽

Vol 231 (3) ◽

pp. 558-569 ◽

Cited By ~ 1

Author(s):

Ning Dong ◽

Binbin Yuan ◽

Pingli Lu

Keyword(s):

Neural Network ◽

Adaptive Learning ◽

Actuator Saturation ◽

Fault Tolerant ◽

Sliding Mode ◽

Learning Algorithm ◽

External Disturbance ◽

Fault Tolerant Control ◽

Lyapunov Theory ◽

Attitude Tracking

In this paper, fault-tolerant attitude tracking control problem is investigated for multiple spacecraft formation flying system with external disturbance, actuator saturation, and faults. A quaternion-based adaptive fault-tolerant control law is proposed based on input normalized neural network. The desired nonlinear smooth function is approximated by using input normalized neural network with an adaptive learning algorithm, and no prior knowledge about spacecraft dynamics is required. Meanwhile, in order to guarantee that the output of input normalized neural network used in the controller is bounded by the corresponding bound of the approximated unknown function, a modified adaptive law is designed to revise the sliding mode manifold. Moreover, the stability of system can be guaranteed by Lyapunov theory. Finally, the validity of the proposed control algorithm is verified through numerical simulations.

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Output feedback fault-tolerant control for a class of nonlinear systems via dynamic gain and neural network

Neural Computing and Applications ◽

10.1007/s00521-019-04583-1 ◽

2019 ◽

Vol 32 (10) ◽

pp. 5517-5530 ◽

Cited By ~ 1

Author(s):

Xiaoye Xi ◽

Tingzhang Liu ◽

Jianfei Zhao ◽

Limin Yan

Keyword(s):

Neural Network ◽

Output Feedback ◽

Control Method ◽

Fault Tolerant ◽

Output Feedback Control ◽

Fault Tolerant Control ◽

Actuator Faults ◽

State Variables ◽

Nonlinear Functions ◽

Dynamic Gain

Abstract In this paper, by combining the dynamic gain and the self-adaptive neural network, an output feedback fault-tolerant control method was proposed for a class of nonlinear uncertain systems with actuator faults. First, the dynamic gain was introduced and the coordinate transformation of the state variables of the system was performed to design the corresponding state observers. Then, the observer-based output feedback controller was designed through the back-stepping method. The output feedback control method based on the dynamic gain can solve the adaptive fault-tolerant control problem when there are simple nonlinear functions with uncertain parameters in the system. For the more complex uncertain nonlinear functions in the system, in this paper, a single hidden layer neural network was used for compensation and the fault-tolerant control was realized by combining the dynamic gain. Finally, the height and posture control system of the unmanned aerial vehicle with actuator faults was taken as an example to verify the effectiveness of the proposed method.

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