A Method of Shaping the Vibro-isolation Properties of Semi-active and Active Systems

Krzyżyński, Tomasz; Maciejewski, Igor; Meyer, Lutz; Meyer, Henning

doi:10.1007/978-3-030-03047-6_8

Cited by 2 publications

(1 citation statement)

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“…The latest developments of vehicle engineering have brought expanding business request to enchain passengers' comfort, and the current broadly used suspension systems can hardly make attained without sacrificing handling stability (Qin et al, 2018). To solve this, active and semi--active suspension frameworks need to be developed as a feasible option and have been broadly studied in the recent years for their better trade-off between comfort and stability (Qin et al, 2018;Krzyżyński and Maciejewski, 2019). Morales et al (2016) designed an active suspension control system with adjustable damping and stiffness by using a magnetorheological (MR) shock absorber and pneumatic system, which achieve the same roll angle levels as a similar passive system while reducing sprung mass acceleration by up to 30%.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based road recognition for intelligent suspension systems

Sun¹,

Cong²

2021

Journal of Theoretical and Applied Mechanics

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This paper presents a deep learning-based road recognition strategy for advanced suspension systems. A four-quarter suspension model with a magnetorheological (MR) damper is developed, and four typical road images with corresponding roughness data are collected. A back-propagation neural network based autoencoder and Convolutional Neural Networks (CNN) are utilized to form the deep learning structure. By utilizing the multi-object genetic algorithm, the optimal parameters can be obtained, and the control current can be adaptively adjusted. Simulation results indicate that the designed structure can identify the road type accurately, and the recognition-based control strategy can improve the suspension performance effectively.

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Section: Introductionmentioning

confidence: 99%