Article: A new control method of automobile's pneumatic suspension systems based on machine learning algorithm Journal: International Journal of Vehicle Systems Modelling and Testing (IJVSMT) 2026 Vol.20 No.1 pp.93 - 114 Abstract: Based on control rules of fuzzy-logic-control (FLC) optimised by genetic-algorithm, a new control method of machine-learning-algorithm (MLA) developed by an adaptive-neuro-fuzzy-inference-system (ANFIS) is used to learn these optimal control rules. MLA is then applied to control automobiles' pneumatic-suspension-systems (PSS) to improve ride comfort. MLA's performance is then evaluated under different conditions on rigid roads and deformed terrains. Some outstanding results include: 1) the ride comfort on the deformed terrain is worse than on the rigid road. Thus, the automobile should move at low speed on the deformed terrain to ensure the comfort; 2) with MLA used, its control performance is relatively stable under all different operating conditions of the automobile; and 3) with control data maps of MLA learned from FLC's optimal rules, MLA improves the comfort better than FLC under various simulation conditions. Therefore, this new control method of MLA should be considered for application in automobile suspension systems. Inderscience Publishers - linking academia, business and industry through research

Title: A new control method of automobile's pneumatic suspension systems based on machine learning algorithm

Authors: Renqiang Jiao; Vanliem Nguyen; Zhang Li

Addresses: School of Mechanical and Electrical Engineering, Hubei Key Laboratory of Intelligent Conveying Technology and Device, Hubei Polytechnic University, Huangshi, 435003, China ' Faculty of Vehicle and Energy Engineering, Thai Nguyen University of Technology, Thai Nguyen, 250000, Vietnam ' School of Mechanical and Electrical Engineering, Jiaozuo University, Jiaozuo, Henan, 454000, China

Abstract: Based on control rules of fuzzy-logic-control (FLC) optimised by genetic-algorithm, a new control method of machine-learning-algorithm (MLA) developed by an adaptive-neuro-fuzzy-inference-system (ANFIS) is used to learn these optimal control rules. MLA is then applied to control automobiles' pneumatic-suspension-systems (PSS) to improve ride comfort. MLA's performance is then evaluated under different conditions on rigid roads and deformed terrains. Some outstanding results include: 1) the ride comfort on the deformed terrain is worse than on the rigid road. Thus, the automobile should move at low speed on the deformed terrain to ensure the comfort; 2) with MLA used, its control performance is relatively stable under all different operating conditions of the automobile; and 3) with control data maps of MLA learned from FLC's optimal rules, MLA improves the comfort better than FLC under various simulation conditions. Therefore, this new control method of MLA should be considered for application in automobile suspension systems.

Keywords: control pneumatic suspensions; automobile's dynamic model; FLC; fuzzy-logic-control; MLA; machine-learning-algorithm; automobile's comfort.

DOI: 10.1504/IJVSMT.2026.152626

International Journal of Vehicle Systems Modelling and Testing, 2026 Vol.20 No.1, pp.93 - 114

Received: 24 Mar 2025
Accepted: 16 Jun 2025
Published online: 31 Mar 2026 *

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Title: A new control method of automobile's pneumatic suspension systems based on machine learning algorithm

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