Neutrosophic Bitopological Modeling for Vibration Control Performance in Vehicle Suspension Systems under Uncertainty

Vibration control is one of the most critical aspects in designing safe and comfortable vehicle suspension systems. Traditional methods such as PID, fuzzy logic, or adaptive control face limitations when dealing with uncertain or incomplete data about road conditions or vehicle dynamics. In this paper, we propose a novel mathematical framework based on generalized open sets in neutrosophic soft bitopological spaces to model and control suspension vibrations under uncertainty. By using two separate neutrosophic topologies, the system's response is evaluated from dual perspectives, capturing both predictable and unpredictable behaviors. Additionally, we incorporate Quadripartitioned Single-Valued Neutrosophic (QSVN) representations to quantify truth, contradiction, ignorance, and falsity of vibration states. This dual-layered structure allows for more precise, flexible, and intelligent decision-making for suspension control. A complete case study using a quarter-car model demonstrates how the proposed framework improves system robustness, especially in environments with noisy or conflicting sensor data.