标题： 基于生物地理学的模糊控制器优化以提高四分之一汽车悬架性能 显示英文标题

标题： Biogeography-Based Optimization of Fuzzy Controllers for Improved Quarter Car Suspension Performance

摘要： 本研究提出了优化的Type-I和Type-II模糊控制器，用于汽车悬架系统，在道路扰动（阶跃/正弦输入）下提高乘坐舒适性和稳定性，解决了现有文献中系统性能比较的不足。 我们整合了基于生物地理学的优化（BBO）、粒子群优化（PSO）和遗传算法（GA）来调整四分之一汽车模型的控制器参数，重点在于BBO尚未被充分探索的有效性。 MATLAB Simulink仿真表明，在阶跃扰动下，BBO优化的Type-II模糊控制相比基线方法将车身位移减少了22%，加速度减少了18%，同时保持计算效率。 该框架为现代车辆提供了实用的高性能解决方案，特别是在振动衰减和能耗效率至关重要的电动汽车和自动驾驶平台中。 显示英文摘要

摘要： This study proposes optimized Type-I and Type-II fuzzy controllers for automotive suspension systems to enhance ride comfort and stability under road disturbances (step/sine inputs), addressing the lack of systematic performance comparisons in existing literature. We integrate Biogeography-Based Optimization (BBO), Particle Swarm Optimization (PSO), and Genetic Algorithms (GA) to tune controller parameters for a quarter car model, with emphasis on BBO's underexplored efficacy. MATLAB Simulink simulations demonstrate that BBO-optimized Type-II fuzzy control reduces body displacement by 22% and acceleration by 18% versus baseline methods under step disturbances, while maintaining computational efficiency. The framework provides practical, high-performance solutions for modern vehicles, particularly electric and autonomous platforms where vibration attenuation and energy efficiency are critical.