Artificial neural network predication and validation of optimum suspension parameters of a passive suspension system

This paper presents the modeling and optimization of quarter car suspension system using Macpherson strut. A mathematical model of quarter car is developed, simulated and optimized in Matlab/Simulink® environment. The results are validated using test rig. The suspension system parameters are optimized using a genetic algorithm for objective functions viz. vibration dose value (VDV), frequency weighted root mean square acceleration (hereafter called as RMS acceleration), maximum transient vibration value, root mean square suspension space and root mean square tyre deflection. ISO 2631-1 standard is adopted to assess ride and health criterion. Results shows that optimum parameters provide ride comfort and health criterions over classical design. The optimization results are experimentally validated using quarter car test setup. The genetic algorithm optimization results are further extended to the artificial neural network simulation and prediction model. Artificial neural network model is carried out in Matlab/Simulink® environment and Neuro Dimensions. Simulation, experimental and predicted results are in close correlation. The optimized system reduces the values of VDV by 45%. Also, RMS acceleration is reduced by 47%. Thus, the optimized system improved ride comfort by reducing RMS acceleration and improved health criterion by reducing the VDV. Finally ANN can be used for predicting the optimum suspension parameters values with good agreement

Optimization of nonlinear quarter car suspension–seat–driver mod

In this paper a nonlinear quarter car suspension–seat–driver model was implemented for optimum design. A nonlinear quarter car model comprising of quadratic tyre stiffness and cubic stiffness in suspension spring, frame, and seat cushion with 4 degrees of freedom (DoF) driver model was presented for optimization and analysis. Suspension system was aimed to optimize the comfort and health criterion comprising of Vibration Dose Value (VDV) at head, frequency weighted RMS head acceleration, crest factor, amplitude ratio of head RMS acceleration to seat RMS acceleration and amplitude ratio of upper torso RMS acceleration to seat RMS acceleration along with stability criterion comprising of suspension space deflection and dynamic tyre force. ISO 2631-1 standard was adopted to assess ride and health criterions. Suspension spring stiffness and damping and seat cushion stiffness and damping are the design variables. Non-dominated Sort Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization – Crowding Distance (MOPSO-CD) algorithm are implemented for optimization. Simulation result shows that optimum design improves ride comfort and health criterion over classical design variables