Learning-Inspired Fuzzy Logic Algorithms for Enhanced Control of Oscillatory Systems

The transportation of sensitive equipment often suffers from vibrations caused by terrain, weather, and motion speed, leading to inefficiencies and potential damage. To address this challenge, this paper explores an intelligent control framework leveraging fuzzy logic, a foundational AI technique, to suppress oscillations in suspension systems. Inspired by learning-based methodologies, the proposed approach utilizes fuzzy inference and Gaussian membership functions to emulate adaptive, human-like decision-making. By minimizing the need for explicit mathematical models, the method demonstrates robustness in both linear and nonlinear systems. Experimental validation highlights the controller’s ability to adapt to varying suspension lengths, reducing oscillation amplitudes and improving stability under dynamic conditions. This research bridges the gap between traditional control systems and learning-inspired techniques, offering a scalable, data-efficient solution for modern transportation challenges.