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Issue title: Artificial Intelligence and Advanced Manufacturing (AIAM 2020)
Guest editors: Shengzong Zhou
Article type: Research Article
Authors: Li, Chongchonga; * | Xiong, Jiangyonga | Liu, Tingshanb | Zhang, Ziangc
Affiliations: [a] School of Intelligent Transportation, Nanjing Vocational College of Information Technology, Nanjing, Jiangsu, China | [b] School of Mechanical Engineering, Yancheng Institute Of Technology, Yancheng, Jiangsu, China | [c] School of Mechanical Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu, China
Correspondence: [*] Corresponding author. Chongchong Li, School of Intelligent Transportation, Nanjing Vocational College of Information Technology, Nanjing, Jiangsu, China. E-mail: [email protected].
Abstract: In order to further improve vehicle ride performance, a dynamic monitoring feedback iteration control algorithm is proposed by combining the features of a variable-damping semi-active suspension system and applying them to the system. A seven-degree-of-freedom finished vehicle simulation model is built based on MATLAB/Simulink. The root-mean-square values of the acceleration of the sprung mass, the dynamic travel of the suspension and the dynamic tire load are taken as evaluation indicators of vehicle ride performance. An analytic hierarchy process (AHP) is used to determine the weighting coefficients of the evaluation indicators, and a genetic algorithm is utilized to determine the optimal damping of the suspension under various typical working conditions. Suspension damping is controlled with a dynamic monitoring feedback iteration algorithm. The correction coefficients of the control algorithm are determined according to the deviation between the obtained damping and the optimized damping so that the control parameters will agree with the optimal result under typical working conditions, and the control effect under other working conditions is verified. The simulation results indicate that the proposed dynamic monitoring feedback iteration control algorithm can effectively reduce the root-mean-square value of the acceleration of the sprung mass by 10.56% and the root-mean-square value of the acceleration of the dynamic travel of the suspension by 11.98% under mixed working conditions, thus improving vehicle ride performance. The study in this paper provides a new attempt for damping control of semi-active suspension and lays a theoretical foundation for its application in engineering.
Keywords: Semi-active suspension, controlled damping, dynamic monitoring feedback iteration, analytic hierarchy process, genetic algorithm, ride performance
DOI: 10.3233/JIFS-189719
Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 3, pp. 4587-4595, 2021
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