Artificial Intelligence Technique Applied to Semi-Active Vibration Control in Vehicle Suspension System

<div class="section abstract"><div class="htmlview paragraph">This study investigates the influence of magnetorheological (MR) dampers in semi-active suspension systems (SASSs) on ride comfort, vehicle stability, and overall performance. Semi-active suspension systems achieve greater flexibility and efficacy by combining MR dampers with the advantages of active and passive suspension systems. The study aims to measure the benefits of MR dampers in improving ride comfort, vehicle stability, and overall system performance. The dynamic system model meets all required performance criteria. This study demonstrates that the proposed artificial intelligence approach, including a fuzzy neural networks proportional-integral-derivative (FNN-PID) controller, significantly enhances key performance criteria when tested under various road profiles. The control performance requirements in engineering systems are evaluated in the frequency and time domains. A quarter-car model with two degrees of freedom (2 DOF) was simulated using MATLAB/Simulink to assess the suggested controller’s performance. The enhanced FNN-PID controller greatly increases ride comfort and vehicle stability when compared to fuzzy neural networks based on PID control strategies proposed, passive suspension systems, uncontrolled MR suspension, and PID controller, according to analysis of the simulated preliminary data. The algorithms' performance is evaluated using a wide range of crucial performance criteria, such as the suspension working space, body mass acceleration, dynamic tire load, and desired force. The phase plane method is used to evaluate system stability. The results clearly show that the proposed controller for the SASS significantly enhances both road holding and ride comfort, highlighting its strong potential for real-world applications.</div></div>