Design of neural network-based control systems for active steering system

被引:0
作者
İkbal Eski
Ali Temürlenk
机构
[1] Erciyes University,Faculty of Engineering, Mechatronics Engineering Department
[2] Ahmet Yesevi University,Faculty of Information Technologies and Engineering
来源
Nonlinear Dynamics | 2013年 / 73卷
关键词
Active steering system; Artificial neural network; Robust control; Random road input signal;
D O I
暂无
中图分类号
学科分类号
摘要
Nowadays, safety of road vehicles is an important issue due to the increasing road vehicle accidents. Passive safety system of the passenger vehicle is to minimize the damage to the driver and passenger of a road vehicle during an accident. Whereas an active steering system is to improve the response of the vehicle to the driver inputs even in adverse situations and thus avoid accidents. This paper presents a neural network-based robust control system design for the active steering system. Primarily, double-pinion steering system used modeling of the active steering system. Then four control structures are used to control prescribed random trajectories of the active steering system. These control structures are as classical PID Controller, Model-Based Neural Network Controller, Neural Network Predictive Controller and Robust Neural Network Predictive Control System. The results of the simulation showed that the proposed neural network-based robust control system had superior performance in adapting to large random disturbances.
引用
收藏
页码:1443 / 1454
页数:11
相关论文
共 50 条
[41]   Neural Network-based Online Fault Diagnosis in Wireless-NoC Systems [J].
Wang, Qi ;
Ouyang, Yiming ;
Lu, Yingchun ;
Liang, Huaguo ;
Zhu, Dakai .
JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS, 2021, 37 (04) :545-559
[42]   Artificial Neural Network-based Technique for Operation Process Control of a Technical Object [J].
Duer, Stanislaw .
DEFENCE SCIENCE JOURNAL, 2009, 59 (03) :305-313
[43]   A Neural Network-based Model Predictive Control Approach for Buildings Comfort Management [J].
Eini, Roja ;
Abdelwahed, Sherif .
2020 IEEE INTERNATIONAL SMART CITIES CONFERENCE (ISC2), 2020,
[44]   Neural network-based H∞ control for fully actuated and underactuated cooperative manipulators [J].
Siqueira, Adriano A. G. ;
Terra, Marco H. .
CONTROL ENGINEERING PRACTICE, 2009, 17 (03) :418-425
[45]   Research on Active vibration control Based on Artificial Neural Network [J].
Wang Hongbo .
2014 Fifth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA), 2014, :789-792
[46]   Artificial Neural Network-based Forest Fire Detection System using Wireless Sensor Network [J].
Chauhan, Anamika ;
Semwal, Sunil ;
Chawhan, Rajneesh .
2013 ANNUAL IEEE INDIA CONFERENCE (INDICON), 2013,
[47]   Artificial neural network-based predictive model for supersonic ejector in refrigeration system [J].
Zhu, Hanzeng ;
Liu, Jiapeng ;
Yu, Jinpeng ;
Yang, Peng .
CASE STUDIES IN THERMAL ENGINEERING, 2023, 49
[48]   Neural Network-Based Optimization of an Acousto Microfluidic System for Submicron Bioparticle Separation [J].
Talebjedi, Bahram ;
Heydari, Mohammadamin ;
Taatizadeh, Erfan ;
Tasnim, Nishat ;
Li, Isaac T. S. ;
Hoorfar, Mina .
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 2022, 10
[49]   Neural Network-Based Mitigation of Nonlinear Inband Distortion in Coded OFDM System [J].
Taherkhani, Nima ;
Kiasaleh, Kamran .
PROCEEDINGS OF THE 2020 IEEE DALLAS CIRCUITS AND SYSTEMS CONFERENCE (DCAS 2020), 2020,
[50]   A New Artificial Neural Network-Based Failure Determination System for Electric Motors [J].
Bicakci, S. ;
Coramik, M. ;
Gunes, H. ;
Citak, H. ;
Ege, Y. .
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2022, 47 (01) :835-847