Novel HCOB3C Optimization Based Fuzzy Logic Controller Design for Experimental Active Suspension System

Abstract

This paper puts forward a novel Hybrid Coyote Optimization-based Big Bang Big Crunch (HCOB³C) algorithm to design an optimal multi-objective fuzzy control framework applied to Active Suspension Systems (ASS). The suspension system in vehicles is an inherent component that is responsible for yielding passenger comfort and ensuring vehicle stability. Since ASS is a multi-objective, constrained non-linear system, the linear controllers will yield suboptimal results because of the so-called bode sensitivity integral problem. Hence, to handle the non-linearity and constraints in the ASS, we present a constrained multi-objective fuzzy controller optimized using the HCOB³C algorithm. The motivation for the proposed hybrid optimization algorithm is that the conventional Big-Bang Big Crunch Optimization (B³CO) and Coyote Optimization (CO) suffer from two major limitations namely 1. Imbalance between exploration and exploitation and 2. Slow convergence respectively. Hence, we utilize the CO to tune the parameters of B³CO to realize optimal actuator force that can offer precise suspension travel and minimize the chassis vibration even in the case of uneven road profile. The performance of the proposed scheme is experimentally validated on a quarter car ASS system for several realistic road profiles. The experimental results substantiate that the proposed scheme can minimize the vehicle vibration by around 41.6% compared to the Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. In general, the HCOB³C-optimized FLC shows a 97% drop in vehicle vibration when compared to a passive system. Moreover, in line with the ISO 2631–1 standards, the analysis of key performance metrics of suspension systems including Frequency-Weighted Root Mean Square (FWRMS) and Vibration Dose Value (VDV) highlights the superiority of the proposed scheme in comparison with the state-of-the-art techniques.

Graphical Abstract