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

TL;DR

This paper introduces a biogeography-based optimization method for tuning fuzzy controllers in quarter car suspension models, significantly improving ride comfort and stability compared to traditional methods.

Contribution

It presents a novel application of BBO for optimizing Type-I and Type-II fuzzy controllers, demonstrating superior performance in vehicle suspension systems.

Findings

BBO-optimized Type-II fuzzy controllers reduce body displacement by 22%.

BBO tuning achieves 18% reduction in acceleration under disturbances.

The approach maintains computational efficiency for real-time applications.

Abstract

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.