Optimisation of Linear Passive Suspension System Using MOPSO and Design of Predictive Tool with Artificial Neural Network

One of the main challenges in the design of passive suspension systems is the optimum selection of suspension system parameters. In this paper, a-four-degree-of-freedom quarter car model is implemented in order to design an optimal suspension system for better ride comfort and road holding characteristics. The mathematical model was generated in MATLAB Simulink environment for simulation. The Multi-objective particle swarm optimisation algorithm is used to optimise the suspension parameters such as suspension spring stiffness, damping coefficient of dampers, driver seat stiffness and driver seat damping coefficient. In addition, an artificial neural network model is trained to predict the root mean square values of ride comfort and road holding characteristics for a given set of input parameters by using the neural network toolbox in MATLAB. The results show that the acceleration of sprung mass and head decayed to a minimum under 2 seconds and the magnitude of the acceleration of the head was lower than that of the sprung mass. The unsprung mass was not displaced from the ground for more than 0.014m and road holding characteristics were also similar.