Active Suspension Systems: Modeling and Comparative Analysis with Passive Systems

In vehicle suspension systems, active suspension models and passive suspension models represent two fundamental approaches to suspension design. Active suspension models employ computer-controlled systems using sensors and actuators to dynamically adjust suspension height, stiffness, and damping characteristics. These systems typically implement real-time control algorithms such as PID controllers or advanced model predictive control (MPC) to enhance ride quality and vehicle stability. The control logic continuously monitors road conditions and vehicle dynamics through accelerometers and position sensors, adjusting suspension parameters accordingly. In contrast, passive suspension models rely solely on the inherent physical properties of suspension components like springs and dampers, without electronic control systems. While passive systems are generally more cost-effective and simpler in design, their adjustment capabilities are inherently limited and cannot adapt to varying road conditions or driving scenarios. The implementation typically involves fixed parameter settings that remain constant regardless of operating conditions. When implementing suspension models in simulation environments like MATLAB/Simulink, active suspension systems require additional blocks for controller design, sensor feedback loops, and actuator models. Key functions might include state-space representations for vehicle dynamics and optimization algorithms for parameter tuning. Passive suspension simulations primarily focus on mechanical component modeling using differential equations representing spring-mass-damper systems. Therefore, selecting the appropriate suspension model is crucial for optimizing vehicle performance and comfort. Active systems offer superior adaptability through sophisticated control algorithms, while passive systems provide reliable performance with simpler implementation and lower computational requirements.