Controlling of 3 DOF car model active suspension system using Adaptive Neuro Fuzzy Inference System controller

Abstract

This research investigates the use of Artificial Intelligence (AI) through an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller for a 3 DOF vehicle model active suspension system, to improve ride comfort and road handling by efficiently managing the suspension system. The 3 DOF quarter car mathematical model is developed using Lagrangian equation, the design of ANFIS controller is developed for active suspension system. Sinusoidal road input is applied for simulation. The MATLAB/Simulink simulation demonstrates the Root Mean Square (RMS) accelerations over time for both passive suspension systems (PSS) and active suspension systems (ASS). The comparative performance of the ASS to the PSS was examined in terms of peak-to-peak displacement and settling time values. For the driver seat mass, sprung mass, and unsprung mass, the ASS achieved improvements of 97.9%, 97.1%, and 89% in peak-to-peak displacement, respectively. The settling time improvements for the driver seat mass, sprung mass, and unsprung mass were 88%, 85%, and 81.8%, respectively. Additionally, the results were evaluated using the root mean square (RMS) acceleration to compare them with the ISO 2631 standard. The findings reveal that the ASS response meets all the qualifications of the ISO 2631 standard for riding comfort.