Open AccessLQR Tuning by Particle Swarm Optimization of Full Car Suspension System
Author(s) -
Muhammad Assahubulkahfi,
Yahaya Md Sam,
Andino Maseleno,
Miftachul Huda
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.13.13479
Subject(s) - particle swarm optimization , control theory (sociology) , suspension (topology) , diagonal , acceleration , sprung mass , ride quality , range (aeronautics) , computer science , engineering , automotive engineering , control (management) , mathematics , algorithm , physics , geometry , classical mechanics , artificial intelligence , homotopy , pure mathematics , aerospace engineering
This paper attempts to examine the potential value in showing the performance of Particle Swarm Optimization (PSO) in order to produce diagonal components of matrix Q, R. The linear model was used in this system, because it has ability to describe all basic performance that exist in full car vehicle suspension system such as roll, pitch, body sprung and each wheel vertical movement. Performance of suspension system measured by range of acceleration arise in automobile body. Drive handling and comfort is an opposite condition. Balancing condition of both define quality of control strategy of suspension system. The disturbances are applied to all tires in testing scenario of applied control algorithm. The simulation result shown better performance of LQR tuning by PSO than passive and LQR without tuning system.