Modelling and optimisation of active front wheel steering system control for armoured vehicle for firing disturbance rejection

While firing on the move, the handling performance of an armoured vehicle is affected, thus causing it to lose its directional stability. This is due to an impulse force generated at the centre of the gun turret, which can produce an unwanted yaw moment at the centre of gravity of the armoured vehicle. In order to reject the unwanted yaw moment, a new hybrid control strategy known as Neural-PI controller had been introduced by combining neural network system and conventional PI controller. This paper developed 14 DOF of armoured vehicle and 2 DOF of Pitman arm steering system. Other than that, determination of the most suitable activation function to be implemented in the Neural-PI controller has been carried out and optimised by using the Genetic Algorithm (GA) method. The performance of the controller was evaluated by comparing the conventional PI controller with the Neural-PI controller implemented with different activation functions.