Open AccessANN Based Controller for Anti-locking Braking System
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f1091.0486s419
Subject(s) - control theory (sociology) , threshold braking , jerk , anti lock braking system , brake , controller (irrigation) , electronic brakeforce distribution , automotive engineering , slip (aerodynamics) , artificial neural network , computer science , electronic stability control , energy (signal processing) , stability (learning theory) , braking system , engineering , control (management) , acceleration , mathematics , aerospace engineering , agronomy , statistics , physics , classical mechanics , artificial intelligence , machine learning , biology
Braking has great impact on the stability of a moving vehicle, as it has to dissipate all the energy that has been stored (kinematic energy) through brake pads (in another forms i.e. heat and sound energy). Stability of the system is more likely to flop as it has to transform and deplete the energy in flash of time, leads to loss in control over desired path followed by drift. Slip (µ) is the key factor to measure stability of this system explicitly, which is defined in terms of vehicle speed (Vs) and wheel speed (Ws). Using Artificial Neural Network (ANN) as a tool to control Anti-lock braking system (ABS) to attain optimal brake pressure thereby minimizing the stopping distance, jerk’s and ultimately system stability. Validation of result were carried out by using MAT-LAB and compared with Hysteresis controller. Simulated results proved that the system performance is improved.