An enhanced adaptive neuro-fuzzy vehicle suspension control in different road conditions

Abstract

Suspension system is an important part of vehicle whose main role is to separate the vehicle body from road induced vibrations. This system can also provide vehicle stability and proper performance. Design and control of a suspension system that can adapt to different road conditions with high flexibility is essential. In this study, data were collected from three types of road conditions with different roughness coefficients in various forward speeds for training a suspension model. Primarily, dynamic equations were derived for a linear full model suspension system. The model is then implemented numerically in MATLAB software. After designing fuzzy controller for active control of actuators, the performance of passive and fuzzy suspension systems in different road conditions were evaluated. The results showed performance improvement of suspension system with fuzzy controller. Then, with the use of fuzzy system simulation data, two adaptive neuro-fuzzy controllers namely grid partitioning and subtractive clustering were trained. Finally, four methods were evaluated and the results showed that decrease in linear deflection and acceleration of vehicle body is higher in adaptive neuro-fuzzy controller by subtractive clustering compared to other systems.