Investigation of Control Behavior via Active Suspension System Considering ‎Time-Delay and Variable Masses

Abstract

The comfort and safety of drivers greatly depend on the design of the suspension system in ‎road cars. The study presents modeling and control of an Active Suspension System (ASS) ‎for a ¼ car with two degrees of freedom, considering unknown masses, time delay, and ‎various types of road disturbances using the MATLAB/Simulink environment. The study ‎examines the performance of the Model ‎Predictive Control (MPC) scheme. ‎Numerous controllers are utilized in literature, including Artificial Neural Networks (ANN), ‎fuzzy logic controllers, Linear Quadratic Regulators (LQR), and Proportional Integral ‎Derivative (PID) controllers. An MPC setup for the ASS model was presented in this paper. ‎MPC is an optimal control strategy that predicts future output using a plant model. The ‎performance of MPC was compared with that of an LQR control method. The results indicate The MPC can produce better road holding, and handling and ride quality are greatly improved by the LQR. The MPC control is 87% faster at eliminating oscillations compared to LQR which is 30%, The conclusion emphasizes that the effectiveness of the MPC ‎scheme is far superior to LQR in all aspects. Despite certain challenges, such as reduced effectiveness under severe overload and increased energy consumption with larger loads.

Keywords

• Active suspension system
• Linear quadratic regulator
• Model ‎predictive control
• Variable masses
• Road disturbance