scholarly journals Research on vehicle adaptive cruise control based on BP neural network working condition recognition

2021 ◽  
Author(s):  
Yongwei Wang ◽  
Zhijun Guo ◽  
Jingbo Wu ◽  
Shenzhen Fu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Adaptive Cruise Control ◽  
Working Condition ◽  
Cruise Control ◽  
Condition Recognition

scholarly journals Neural Network Optimized Model Predictive Multi-Objective Adaptive Cruise Control

2018 ◽  
Vol 166 ◽  
pp. 01009
Author(s):  
Siyi Zhang ◽  
Junzhi Zhang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Computational Complexity ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Radar Sensor ◽  
Multi Objective ◽  
The Neural Network ◽  
Sensor Models ◽  
Optimized Model

A model predictive multi-objective adaptive cruise control (MPC MO-ACC) system, designed to consider both the tracking performance and the fuel consumption, is optimized by a neural network in this paper, reducing the computational complexity without sacrificing the control performance. The optimized MO-ACC control system is built by training a neural network with the control results of the MPC MO-ACC system. Simulation tests are conducted in Matlab/Simulink in conjunction with the high-fidelity CarMaker software. Influences of four driving conditions (the learning track, NEDC, JP05, FTP75) and two kinds of sensor models (ideal radar sensor and 77GHz physical radar sensor) are analysed. Simulation results have shown that the neural network optimized model predictive MO-ACC has the same control capability and strong robustness as the original MPC MO-ACC. Meanwhile, the optimized control system has much lower computational complexity, which shows potentials for the application in real-time vehicle control and industry.

Synthesis and Approximation of Control in Adaptive Cruise Control Systems of Commercial Vehicles

2018 ◽  
Vol 19 (11) ◽  
pp. 707-713 ◽  
Author(s):  
V. G. Volkov ◽  
D. N. Demyanov
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Systems ◽  
Adaptive Cruise Control ◽  
Commercial Vehicle ◽  
Pi Controller ◽  
Cruise Control ◽  
Cruise Control System ◽  
Wide Range ◽  
Driving Conditions

In this paper, we consider the problem of the development of an algorithm of the adaptive cruise control functioning operating in the conditions of powertrain gear ratio varying in a wide range and vehicle velocity changing. The functioning of a classical cruise control system is generally based on the usage of a PID-controller with constant coefficients. However, despite the easiness of its tuning and physical realization and also its relatively high robustness this class of control devices cannot guarantee the cruise control system optimal functioning in all driving conditions because the plant is not timeinvariant and linear. To overcome the above shortcomings, in this research we consider the possibility of neural network realization of a commercial vehicle adaptive cruise control algorithm.In this paper, we propose the mathematical model of a commercial vehicle longitudinal motion designed for the control system analysis and synthesis. We carry out the PI-controller coefficients tuning to control the vehicle longitudinal velocity in various driving conditions of a commercial vehicle. We show that the controller coefficients vary according to a rather complex law. Therefore, we propose the algorithm of the adaptive cruise control functioning based on the approximation of the controller coefficients by the artificial neural network. The network used is the multilayer perceptron and it has ten neurons in the hidden layer to provide the high quality of the approximation. We carry out the training of the neural network by the Levenberg-Marquardt method with a sample of a total volume of 500 points, obtained using standard methods of controller synthesis. We verify the correctness of the obtained results through the computer simulations of the vehicle acceleration from 0 to 100 km/h, proving that the PI-controller coefficients, providing the required transient responses, significantly vary depending on the current state of the vehicle. The approach of the PI-controller coefficients approximation presented in this paper may be further used in the design of adaptive control systems able to function effectively in various operating modes.

Fault Monitoring of Transformer Based on FTIR

2011 ◽  
Vol 383-390 ◽  
pp. 5094-5099
Author(s):  
Shu Bo Jiang
Keyword(s):  
Neural Network ◽  
Quantitative Analysis ◽  
Bp Neural Network ◽  
Working Condition ◽  
Power Transmission ◽  
Safety Level ◽  
Fault Monitoring ◽  
Transmission Equipment ◽  
Infrared Spectrometer ◽  
Potential Failure

Transformer is important power transmission equipment, and its working condition directly affects the safety level. Using the Fourier transformer infrared spectrometer for the qualitative analysis of the fault gases, using the theory of BP neural network for the quantitative analysis of the characteristics of the fault gases, can determine the operational status of transformer. This method monitors the function of the transformer effectively, judges the potential failure or hidden dangers accurately.

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