Research on fault tolerant control system based on optimized neural network algorithm

2020 ◽  
Vol 39 (6) ◽  
pp. 9073-9083
Author(s):  
Xianming Shan ◽  
Huixin Liu ◽  
Yefeng Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control System ◽  
Fault Diagnosis ◽  
Network Model ◽  
Fault Tolerant ◽  
Rapid Development ◽  
Fault Tolerant Control ◽  
Control Measures ◽  
Depth Sensor

Due to the strict personnel control measures in COVID-19 epidemic, the control system cannot be maintained and managed manually. This puts forward higher requirements for the accuracy of its fault-tolerant performance. The control system plays an increasingly important role in the rapid development of industrial production. When the sensor in the system fails, the system will become unstable. Therefore, it is necessary to accurately and quickly diagnose the faults of the system sensors and maintain the system in time. This paper takes the control system as the object to carry out the fault diagnosis and fault-tolerant control research of its sensors. A network model of wavelet neural network is proposed, and an improved genetic algorithm is used to optimize the weights and thresholds of the neural network model to avoid the deficiencies of traditional neural network algorithms. For the depth sensor of a certain system, an online fault diagnosis scheme based on RBF (Radial Basis Function) neural network and genetic algorithm optimized neural network was designed. The disturbance fault, “stuck” fault, drift fault and oscillation fault of the depth sensor are simulated. Simulation experiments show that both online fault diagnosis schemes can accurately identify sensor faults and the genetic algorithm optimized neural network is superior to RBF neural network in both recognition accuracy and training time under the influence of COVID-19.

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.

Study on Intelligent Self-Adaptive Control System

2014 ◽  
Vol 539 ◽  
pp. 620-624
Author(s):  
Ze Min Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control System ◽  
Pid Controller ◽  
Closed Loop ◽  
Rapid Development ◽  
Adaptive Control System ◽  
Positive Role ◽  
The Neural Network ◽  
Control Of Parameters

With the rapid development of China's industry, the use of the control system has become more and more extensive. However, with the complicating of the production system, the traditional control system has been unable to meet the needs of the current industry. Effectively bring the genetic algorithm of the neural network into the control system can solve this problem. Here, it firstly describes the neural network, genetic algorithm principle, operation procedures and the characteristics; secondly, analyzes the principle and lack of conventional PID controller; finally, effectively combines genetic algorithm and controller together, forming a closed loop, strengthening the control of parameters, and giving a code description of the genetic algorithm. This paper plays a certain positive role for industrial engineers and programmers.

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