Enhancing skyhook for semi-active suspension control via machine learning

Savaia, Gianluca; Formentin, Simone; Panzani, Giulio; Corno, Matteo; Savaresi, Sergio M.

doi:10.1016/j.ifacsc.2021.100161

Cited by 12 publications

(3 citation statements)

References 19 publications

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“…This control method is being developed strongly in recent years. [16][17][18] In this study, the semiactive suspensions using machine learning (SS-ML) of the vehicle is also researched and applied.…”

Section: Control Methods Of Machine Learningmentioning

confidence: 99%

“…Nowadays, machine learning technology is being researched and applied in control fields. [16][17][18] Based on the pre-designed input data map of the control system, the Neuro-adaptive-learning of machine learning can be trained to learn all data map in the control system and automatically optimize its control performance in all different working conditions of the system. Therefore, based on the control rules in fuzzy control built from the data of different road surface excitations, this control rule data map can feed the machine learning process to control the vehicle's semiactive suspensions.…”

Section: Introductionmentioning

confidence: 99%

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Controlling performance of semiactive suspension with two methods of fuzzy-control and machine-learning

Zhou,

Liu,

et al. 2024

Noise & Vibration Worldwide

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Two different methods of Fuzzy-control and Machine-learning are proposed to control the vehicle’s semiactive suspensions. To control performance of semiactive suspensions using machine learning (SS-ML) and semiactive suspensions using fuzzy control (SS-FC), a half-vehicle model has been established to calculate and simulate vibration equations under random road surfaces from ISO A-class to F-class. Via map of control rule data in SS-FC established at roads ISO A-class, B-class, …, and F-class, SS-ML’s Neuro-Adaptive-Learning has been trained for learning these control rules. The results obtained indicate that under the same road surface excitation of ISO C-class, the control performance of SS-FC and SS-ML is equivalent., and the vehicle’s comfort level using both SS-FC and SS-ML is very well improved in comparison with passive suspensions without control (PS-WC) of the vehicle. Under a mixed road surface from ISO A-class to F-class and a change range of the vehicle’s moving velocity from 2.5 m s−1 to 35m s−1 used for simulation, the vehicle’s comfort level using SS-ML is better than vehicle’s comfort level using SS-FC. Especially, the root mean square values of displacements and accelerations in vertical and pitch directions of the vehicle body with SS-ML are smaller than that of SS-FC by 13.4%, 23.2%, 20.7%, and 14.3%, respectively. Therefore, the control performance of SS-ML is better than SS-FC, and it should be used to control the vehicle’s semiactive suspensions for enhancing the comfort level.

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Section: Control Methods Of Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Controlling performance of semiactive suspension with two methods of fuzzy-control and machine-learning

Zhou,

Liu,

et al. 2024

Noise & Vibration Worldwide

View full text Add to dashboard Cite

show abstract

“…Semi-active suspensions are far superior in achieving damping control on varying road profiles compared with passive suspension systems [6]. Semi-active suspensions possess components of passive systems, but the differentiating factor between the two systems is that the semi-active systems dissipate the energy, making the system intrinsically stable [7]. Hamersma et al discovered that an anti-lock braking system (ABS) performed better when fitted with semi-active suspension [8].…”

Section: Introductionmentioning

confidence: 99%

A Review on Various Control Strategies and Algorithms in Vehicle Suspension Systems

Ferhath,

Kamalakkannan Kasi

2023

Int. J. Automot. Mech. Eng.

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Automotive suspension systems provide passenger safety, ride comfort and vehicle handling in passenger and commercial vehicles. Through extensive research in the last couple of decades coupled with the recent advancements in technology, the improvement in vehicle handling and ride comfort have been significant by using various control strategies in semi-active and fully active suspension systems. Despite a significant number of articles available on the enhancement and improvement of vehicle suspension systems, there is certainly a lack of knowledge on various control strategies and algorithmic techniques used in the vehicle suspension system. Thereby, to address the gap, this review consecutively attempts to comprehensively explore the various research work conducted on the various control strategies used in vehicle suspension systems.

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Road preview MPC of semi‐active suspension with magneto‐rheological damper

Guo

et al. 2023

Intl J Robust & Nonlinear

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Enhancing skyhook for semi-active suspension control via machine learning

Cited by 12 publications

References 19 publications

Controlling performance of semiactive suspension with two methods of fuzzy-control and machine-learning

Controlling performance of semiactive suspension with two methods of fuzzy-control and machine-learning

A Review on Various Control Strategies and Algorithms in Vehicle Suspension Systems

Road preview MPC of semi‐active suspension with magneto‐rheological damper

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