DocumentCode
2507331
Title
The neural compensator for advance vehicle controller
Author
Rodic, Aleksandar ; Katic, Dusko ; Vukobratovic, Miomir
Author_Institution
Robotics Lab., Mihajlo Pupin Inst., Belgrade, Serbia
fYear
2002
fDate
26-28 Sept. 2002
Firstpage
95
Lastpage
100
Abstract
In this paper, a new concept of the advanced integrated vehicle controller with a 4-wheel control system (ADIVEC-4WCS), to provide an automatic system guidance, is presented. The supplementary neuro-compensator is proposed to ensure a control system robustness and better controller adaptability upon the system uncertainties and model inaccuracies. This neural compensator is a part of integrated active control algorithm based on the centralized dynamic control strategy and full vehicle model. The fast convergence of learning process is achieved using standard back propagation method. The validity and effectiveness of the proposed method based on adaptive capability of neural compensator for a four wheel steering system have been demonstrated by simulation experiments.
Keywords
adaptive control; backpropagation; compensation; neurocontrollers; road vehicles; robust control; transport control; uncertain systems; 4-wheel control system; ADIVEC-4WCS; advanced integrated vehicle controller; automatic system guidance; centralized dynamic control strategy; control system robustness; controller adaptability; four wheel steering system; full vehicle model; integrated active control algorithm; learning process convergence; model inaccuracies; neural compensator; neuro-compensator; standard back propagation method; standard backpropagation method; system uncertainties; Automatic control; Centralized control; Control system synthesis; Control systems; Convergence; Intelligent vehicles; Navigation; Robust control; Uncertainty; Vehicle dynamics;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Network Applications in Electrical Engineering, 2002. NEUREL '02. 2002 6th Seminar on
Print_ISBN
0-7803-7593-9
Type
conf
DOI
10.1109/NEUREL.2002.1057976
Filename
1057976
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