Fault-tolerant control of rotor/magnetic bearing systems using reconfigurable control with built-in fault detection

2000 ◽  
Vol 214 (12) ◽  
pp. 1445-1465 ◽  
Author(s):  
M O T Cole ◽  
P S Keogh ◽  
C R Burrows
Keyword(s):  
Neural Network ◽  
Control Systems ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Industrial Applications ◽  
Magnetic Bearing ◽  
Control Input ◽  
Sensor Failure ◽  
The Neural Network ◽  
And Performance

Magnetic bearings now exist in a variety of industrial applications. However, there are still concerns over the control integrity of rotor/magnetic bearing systems and the ability of control systems to cope with possible faults that can occur during operation. Unless control systems can be developed that have the ability to maintain safe operation when the system is in a degraded or faulty state, then many, otherwise viable, magnetic bearing applications will remain unfulfilled. In this paper, a method is proposed for the design of a fault-tolerant control system that can detect and identify both incipient and sudden faults as and when they occur. A multivariable H∞ controller is reconfigured on occurrence of a fault so that stability and performance is maintained. A neural network is trained to identify faults associated with the system position transducer measurements so that the output from the neural network can be used as the decision tool for reconfiguring control. In this way, satisfactory control of the system can be maintained during failure of a control input. The method requires no knowledge of the system dynamics or system disturbances, and the network can be trained on-line. The validity of this method is demonstrated experimentally for various modes of sensor failure.

Neural Network Based Fault Tolerant Control for a Semi-Active Suspension

2019 ◽  
Author(s):  
Sergio Alberto Rueda Villanoba ◽  
Carlos Borrás Pinilla
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Tolerant ◽  
Mr Damper ◽  
Fault Tolerant Control ◽  
Active Suspension ◽  
Suspension System ◽  
Vehicle Body ◽  
Force Model ◽  
The Neural Network

Abstract In this study a Neural Network based fault tolerant control is proposed to accommodate oil leakages in a magnetorheological suspension system based in a half car dynamic model. This model consists of vehicle body (spring mass) connected by the MR suspension system to two lateral wheels (unsprung mass). The semi-active suspension system is a four states nonlinear model; it can be written as a state space representation. The main objectives of a suspension are: Isolate the chassis from road disturbances (passenger comfort) and maintain contact between tire and road to provide better maneuverability, safety and performance. On the other hand, component faults/failures are inevitable in all practical systems, the shock absorbers of semi-active suspensions are prone to fail due to fluid leakage but quickly detect and diagnose this fault in the system, avoid major damage to the system and ensure the safety of the driver. To successfully achieve desirable control performance, it is necessary to have a damping force model which can accurately represent the highly nonlinear and hysteretic dynamic of the MR damper. To simulate parameters of the damper, a quasi-static model was applied, quasi-static approaches are based on non-newtonian yield stress fluids flow by using the Bingham MR Damper Model, relating the relative displacement of the piston, the frictional force, a damping constant, the stiffness of the elastic element of the damper and an offset force. The Fault detection and isolation module is based on residual generation algorithms. The residua r is computed as the difference between the displacement signal of functional and faulty model, when the residual is close to zero, the process is free of faults, while any change in r represents a faulty scheme then a wavelet transform, (Morlet wave function) is used to determine the natural frequencies and amplitudes of displacement and acceleration signal during the failure, this module provides parameters to the neural network controller in order to accommodate the failure using compensation forces from the remaining healthy damper. The neural network uses the error between the plant output and the neural network plant for computing the required electric current to correct the malfunction using the inverse dynamics function of the MR damper model. Consequently, a bump condition, and a random profile road (ISO 8608) described by the power spectral density (PSD) of its vertical displacement, is used as disturbance of control system. The performance of the proposed FTC structure is demonstrated trough simulation. Results shows that the control system could reduce the effect of the partial fault of the MR Damper on system performance.

scholarly journals Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control

2021 ◽  
Vol 4 (3) ◽  
pp. 51
Author(s):  
Junxia Yang ◽  
Youpeng Zhang ◽  
Yuxiang Jin
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Fault Tolerant Control ◽  
Control Input ◽  
Terminal Sliding Mode ◽  
Train Operation ◽  
Automatic Train Operation

Aiming at the problem of the large tracking error of the desired curve for the automatic train operation (ATO) control strategy, an ATO control algorithm based on RBF neural network adaptive terminal sliding mode fault-tolerant control (ATSM-FTC-RBFNN) is proposed to realize the accurate tracking control of train operation curve. On the one hand, considering the state delay of trains in operation, a nonlinear dynamic model is established based on the mechanism of motion mechanics. Then, the terminal sliding mode control principle is used to design the ATO control algorithm, and the adaptive mechanism is introduced to enhance the adaptability of the system. On the other hand, RBFNN is used to adaptively approximate and compensate the additional resistance disturbance to the model so that ATO control with larger disturbance can be realized with smaller switching gain, and the tracking performance and anti-interference ability of the system can be enhanced. Finally, considering the actuator failure and the control input limitation, the fault-tolerant mechanism is introduced to further enhance the fault-tolerant performance of the system. The simulation results show that the control can compensate and process the nonlinear effects of control input saturation, delay, and actuator faults synchronously under the condition of uncertain parameters, external disturbances of the system model and can achieve a small error tracking the desired curve.

Recent advances in active fault tolerant flight control systems

2021 ◽  
pp. 095441002110628
Author(s):  
Muhammad Sohail Khan Raja ◽  
Qasim Ali
Keyword(s):  
Control Systems ◽  
Flight Control ◽  
Active Fault ◽  
Fault Tolerant ◽  
State Of The Art ◽  
Fault Tolerant Control ◽  
System Component ◽  
Future Research ◽  
Sensor Failure ◽  
Art Research

The Flight Control System (FCS) is considered as the brain of an aerial vehicle. It is a mechanism through which pilot’s commands are transferred to the actuators of the aircraft control surfaces. In order to ensure safety and increase reliability of aerial vehicles, development of fault tolerant FCSs has been the focus of research community for past few decades. Fault tolerant ability enables an aircraft to maintain satisfactory performance even in the state of a fault. Fault Tolerant Control Systems (FTCS) are categorized as passive and active control systems. Passive FTCS are designed to mitigate the effects of certain known faults. These faults can be related to sensor failure, actuator failure, or system component failure. On the other hand, active FTCS contain a controller reconfiguration mechanism, whereby, they can adjust the controller input online to mitigate the effects of the faults. In this way, they can accommodate complicated and versatile faults as compared to their passive counterparts. This paper presents a review of significant research during last decade in active fault tolerant control with applications to FCSs. A review of state-of-the-art works in this domain has also been presented. Upon review, these state-of-the-art research interests have been categorized into respective categories. Furthermore, research works have been cataloged based on their technology readiness levels. Based on these reviews, future research directions have also been highlighted.

Robust fault-tolerant control for uncertain robot manipulators based on adaptive quasi-continuous high-order sliding mode and neural network

2015 ◽  
Vol 229 (8) ◽  
pp. 1425-1446 ◽  
Author(s):  
Mien Van ◽  
Hee-Jun Kang
Keyword(s):  
Neural Network ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Robot Manipulators ◽  
Fault Tolerant Control ◽  
Stability And Convergence ◽  
Fault Estimation ◽  
The Neural Network ◽  
Fault Information ◽  
Uncertain Robot

This paper investigates a robust fault-tolerant control scheme for uncertain robot manipulators. The proposed scheme is designed via active fault-tolerant control method by combining a fault estimation scheme with a novel robust adaptive quasi-continuous second-order sliding mode (AQC2S) controller, so as to accommodate not only system failures but also uncertainties. First, a neural network based fault estimation is designed to online approximate the unknown uncertainties and faults. The estimated uncertainty and fault information are then used to compensate in advance for the effects of uncertainties in fault-free operation and both uncertainties and faults in fault operation. To eliminate the neural network compensation error, QC2S with adaptation gain, named as adaptive QC2S (AQC2S), is proposed. By integrating the advantages of the neural network observer and the AQC2S controller, the integrated scheme has a good capability to accommodate both the uncertainties and faults with chattering-free, higher position tracking accuracy, and no requirement of prior knowledge of the fault information. The stability and convergence of the proposed fault-tolerant control system is proved theoretically. Simulation results for a PUMA560 robot demonstrate the effectiveness of the proposed algorithm.

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