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Article

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

School of Mechanical & Automobile Engineering, Qingdao University of Technology, No. 777 Jialingjiang Road, Qingdao 266520, China
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Author to whom correspondence should be addressed.
Academic Editors: Ming Wu, Hongling Sun and Yijing Chu
Appl. Sci. 2021, 11(21), 10493; https://doi.org/10.3390/app112110493
Received: 8 September 2021 / Revised: 21 October 2021 / Accepted: 4 November 2021 / Published: 8 November 2021
(This article belongs to the Special Issue Application of Active Noise and Vibration Control)
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. View Full-Text
Keywords: active suspension; machine learning; LQR control; K-means clustering; genetic algorithm active suspension; machine learning; LQR control; K-means clustering; genetic algorithm
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MDPI and ACS Style

Wu, K.; Liu, J.; Li, M.; Liu, J.; Wang, Y. Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm. Appl. Sci. 2021, 11, 10493. https://doi.org/10.3390/app112110493

AMA Style

Wu K, Liu J, Li M, Liu J, Wang Y. Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm. Applied Sciences. 2021; 11(21):10493. https://doi.org/10.3390/app112110493

Chicago/Turabian Style

Wu, Kun, Jiang Liu, Min Li, Jianze Liu, and Yushun Wang. 2021. "Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm" Applied Sciences 11, no. 21: 10493. https://doi.org/10.3390/app112110493

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