Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm

The traditional Linear quadratic regulator (LQR) control algorithm depends too much on expert experience during the selection of weighting coefficients. To solve this problem, we proposed a Genetic K-means clustering Linear quadratic (GKL) algorithm. Firstly, a 2-DOF 1/4 vehicle model and road input...

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Autores principales:	Kun Wu, Jiang Liu, Min Li, Jianze Liu, Yushun Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	active suspension machine learning LQR control K-means clustering genetic algorithm Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/ac4376cedf2f42edbbb2f0df991d8858
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id	oai:doaj.org-article:ac4376cedf2f42edbbb2f0df991d8858
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spelling	oai:doaj.org-article:ac4376cedf2f42edbbb2f0df991d88582021-11-11T15:25:50ZMulti-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm10.3390/app1121104932076-3417https://doaj.org/article/ac4376cedf2f42edbbb2f0df991d88582021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10493https://doaj.org/toc/2076-3417The traditional Linear quadratic regulator (LQR) control algorithm depends too much on expert experience during the selection of weighting coefficients. To solve this problem, we proposed a Genetic K-means clustering Linear quadratic (GKL) algorithm. Firstly, a 2-DOF 1/4 vehicle model and road input model are established. The weights of an LQR controller are optimized using a genetic algorithm. Then, a possible weighting space is constructed based on this optimal solution. Random weighting coefficients of each performance index are generated in this space. Next, LQR control for the 1/4 vehicle model is performed, and the simulation data are recorded automatically, with these random weighting values, different road classes, and driving speed. A machine learning dataset is built from these simulations. Finally, a K-means clustering algorithm is used to classify the LQR control active suspension into three performance modes: safety mode, comprehensive mode, and comfort mode. The optimal weighting matrix of each performance mode is determined to satisfy requirements for different types of drivers. The results show that the new GKL algorithm not only improves the suspension control effect but also realizes different performance modes. It can better adapt to the changes in driving conditions and drivers.Kun WuJiang LiuMin LiJianze LiuYushun WangMDPI AGarticleactive suspensionmachine learningLQR controlK-means clusteringgenetic algorithmTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10493, p 10493 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	active suspension machine learning LQR control K-means clustering genetic algorithm Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	active suspension machine learning LQR control K-means clustering genetic algorithm Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Kun Wu Jiang Liu Min Li Jianze Liu Yushun Wang Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm
description	The traditional Linear quadratic regulator (LQR) control algorithm depends too much on expert experience during the selection of weighting coefficients. To solve this problem, we proposed a Genetic K-means clustering Linear quadratic (GKL) algorithm. Firstly, a 2-DOF 1/4 vehicle model and road input model are established. The weights of an LQR controller are optimized using a genetic algorithm. Then, a possible weighting space is constructed based on this optimal solution. Random weighting coefficients of each performance index are generated in this space. Next, LQR control for the 1/4 vehicle model is performed, and the simulation data are recorded automatically, with these random weighting values, different road classes, and driving speed. A machine learning dataset is built from these simulations. Finally, a K-means clustering algorithm is used to classify the LQR control active suspension into three performance modes: safety mode, comprehensive mode, and comfort mode. The optimal weighting matrix of each performance mode is determined to satisfy requirements for different types of drivers. The results show that the new GKL algorithm not only improves the suspension control effect but also realizes different performance modes. It can better adapt to the changes in driving conditions and drivers.
format	article
author	Kun Wu Jiang Liu Min Li Jianze Liu Yushun Wang
author_facet	Kun Wu Jiang Liu Min Li Jianze Liu Yushun Wang
author_sort	Kun Wu
title	Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm
title_short	Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm
title_full	Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm
title_fullStr	Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm
title_full_unstemmed	Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm
title_sort	multi-mode active suspension control based on a genetic k-means clustering linear quadratic algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ac4376cedf2f42edbbb2f0df991d8858
work_keys_str_mv	AT kunwu multimodeactivesuspensioncontrolbasedonagenetickmeansclusteringlinearquadraticalgorithm AT jiangliu multimodeactivesuspensioncontrolbasedonagenetickmeansclusteringlinearquadraticalgorithm AT minli multimodeactivesuspensioncontrolbasedonagenetickmeansclusteringlinearquadraticalgorithm AT jianzeliu multimodeactivesuspensioncontrolbasedonagenetickmeansclusteringlinearquadraticalgorithm AT yushunwang multimodeactivesuspensioncontrolbasedonagenetickmeansclusteringlinearquadraticalgorithm
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Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm

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