Hybrid control strategy for a semi active suspension system using fuzzy logic and bio-inspired chaotic fruit fly algorithm

TL;DR

This paper introduces a hybrid control approach combining fuzzy logic and bio-inspired chaotic algorithms to improve semi-active suspension systems, significantly enhancing ride comfort and handling over traditional methods.

Contribution

It develops a novel hybrid PID-fuzzy control scheme with chaotic fruit fly algorithm tuning, outperforming existing stochastic optimization methods in suspension control.

Findings

83.3% reduction in peak acceleration

28.57% reduction in suspension distortion

Superior performance compared to GA, PSO, and BFO

Abstract

This study proposes a control strategy for the efficient semi active suspension systems utilizing a novel hybrid PID-fuzzy logic control scheme .In the control architecture, we employ the Chaotic Fruit Fly Algorithm for PID tuning since it can avoid local minima by chaotic search. A novel linguistic rule based fuzzy logic controller is developed to aid the PID.A quarter car model with a non-linear spring system is used to test the performance of the proposed control approach. A road terrain is chosen where the comfort and handling parameters are tested specifically in the regions of abrupt changes. The results suggest that the suspension systems controlled by the hybrid strategy has the potential to offer more comfort and handling by reducing the peak acceleration and suspension distortion by 83.3 % and 28.57% respectively when compared to the active suspension systems. Also, compared to the performance of similar suspension control strategies optimized by stochastic algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO), reductions in peak acceleration and suspension distortion are found to be 25%, 32.3%, 54.6% and 23.35 %, 22.5%, 5.4 % respectively.The details of the solution methodology have been presented in the paper.