Int. J. of Vehicle Autonomous Systems   »   2017 Vol.13, No.4

 

 

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

 

Authors: Mazuan Mansor; Khisbullah Hudha; Zulkiffli Abd Kadir; Noor Hafizah Amer; Vimal Rau Aparow

 

Addresses:
Faculty of Engineering, Department of Mechanical Engineering, National Defence University of Malaysia, Kem Sungai Besi, Kuala Lumpur, Malaysia
Faculty of Engineering, Department of Mechanical Engineering, National Defence University of Malaysia, Kem Sungai Besi, Kuala Lumpur, Malaysia
Faculty of Engineering, Department of Mechanical Engineering, National Defence University of Malaysia, Kem Sungai Besi, Kuala Lumpur, Malaysia
Faculty of Engineering, Department of Mechanical Engineering, National Defence University of Malaysia, Kem Sungai Besi, Kuala Lumpur, Malaysia
Faculty of Engineering, Department of Mechanical Engineering, National Defence University of Malaysia, Kem Sungai Besi, Kuala Lumpur, Malaysia

 

Abstract: 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.

 

Keywords: 14 DoF; active front wheel steering; firing disturbance; neural network; genetic algorithm; activation function.

 

DOI: 10.1504/IJVAS.2017.10008213

 

Int. J. of Vehicle Autonomous Systems, 2017 Vol.13, No.4, pp.306 - 329

 

Date of acceptance: 05 Mar 2017
Available online: 03 Oct 2017

 

 

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