Model in the loop simulation of an active front wheel steering system for wheeled armored vehicle

During firing on the move, handling performance of an armored vehicle will be affected which causing it to lose its directional stability. This is due to an impulse force created at the center of gun turret, which produce an unwanted yaw moment at the center of gravity (COG) of the armored vehicle. The unwanted yaw motion created cause the directional stability of the armored vehicle is violated where it will sway from its intended path without any input from driver. To reject the unwanted yaw moment in purposed to improve the handling ability of the armored and also to make it able to perform firing while moving, this study deals with proposing a new Active Front Wheel Steering
(AFWS) system actuator which consists of Ravigneaux planetary gear which was previously applied in automotive transmission system. This study focused on developing
a control strategy for the AFWS system in order to reduce unwanted yaw motion created by armored vehicle during the execution of firing using gun turret system. This study
also includes the explanation of the design, working principle and the derivation of the planetary gear mathematical model based on its dynamic behavior. The mathematical model is validated with the actual system to assess the model validity. The proposed AFWS actuator is then implemented into Pitman arm steering system test rig to analyze its robustness and functionality using position tracking control method. The position tracking control method is conducted using Model-in-Loop simulation which consists of Software-in-Loops simulation (SILs) and Hardware-in-Loop simulation (HILs). SILs is applied to the position tracking control in order to validate the mathematical model developed while HILs is used to test the functionality of the proposed AFWS actuator in actuating the steering system. The proposed control strategy consists of PI controller tuned by neural network system which is named as Neuro-PI controller. The Neuro-PI controller is optimized by Genetic algorithm optimization tools to obtained the most
optimum activation function to be applied in the neural network system. The optimum neural system is selected based on its performance in controlling the handling stability
of armored vehicle in reducing the unwanted yaw motion. The Neuro-PI controller with Hardlims activation function shows a better performance which able to reduce up to 40% of unwanted yaw motion compared to other activation function. The robustness of the optimum controller is tested using HILs together with the implementation of the proposed AFWS actuator. The result from the experiment shows that both the proposed AFWS actuator and the controller can be applied in the armored vehicle to improve the handling stability by reducing the yaw motion produced during the execution of firing while in motion.