The modelling and control of the drive system of an Ackermann Robot using GA optimization
This paper provides the mathematical modelling and control optimization, of the drive system of an Ackermann four wheeled autonomous robot, with Genetic algorithm used for tuning the proportional, integral and derivative (PID) Controller parameters. The aim and main objective of this work is focused on the control of the driving speed input from the rear wheels of the robot and control. The robot drive in proportion to obstacle input ahead of the four wheeled chassis using genetic algorithms. A controlled platform that can be deployed for driverless vehicle in the nearest future and military unmanned vehicle is our major concern. The controlled system response stabilized in 0.675 seconds, after exciting the system with a step response. Variation for the system also shows, that the cost function was minimized or adjusted to obtain optimal PID parameters as Proportional (P) = 12.671, Integral (I) = -0.399, Derivative (D) = 1477561, at a value of 9.6778*10-4.
Keywords: Ackermann steering, modelling, optimization, Genetic Algorithm, control, PID, driverless vehicle