Ant Colony Optimization Tuned Closed-Loop Optimal Control Intended for Vehicle Active Suspension System

In recent years, the suspension system in modern vehicles has played a key role both as far driving safety and comfort is concerned. To satisfy these vehicle performance specifications, active currently studied implemented practice decades. contrast to passive suspensions, by introducing force into system, can alter dynamic real-time. A design of controller needed for real-time tuning control an (ASS) fulfill challenging objectives comprising road handling, ride convenience, travel suspension. This research proposed novel ant colony optimization (ACO) algorithm solving multi-objective weight problem linear quadratic regulator (LQR) automobiles ASS. The ASS state-feedback (SFC) result ACO used find optimal LQR weights. Here Q R matrix tuned. On quarter-car (QCASS) effectiveness ACO-tuned analytically checked with hardware loop (HIL) analysis irregular surface. Here, experiment, ISO D rough runway, bumpy path, pulse-type profile are taken account. Experimental findings illustrate that procedure substantially reduce acceleration Car body due profiles compared classical tuned model predictive (MPC). shows profound impact on efficiency schemes three different profiles.