Improvement of ride performance with an active suspension based on fuzzy logic control

Nan, Yanghai; Shi, Wei; Fang, Pan Yu

doi:10.21595/jve.2016.16827

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Wei et al [94] applied fuzzy PID control on vehicles and achieved an AVB 4 (k) optimization of 3.54%. Chen et al [96] achieved a 68.4% optimization of AVB 4 (k), and Nan et al [107] achieved a 35% optimization of AVB 4 (k) using fuzzy logic control. The maximum AVB 4 (k) optimization was 89.41%, which appeared in the study by Gomonwattanapanich et al [83].…”

Section: Comparison With the Existing Literaturementioning

confidence: 99%

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

Yin,

Wang,

et al. 2024

WEVJ

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The suspension system is a crucial part of an electric vehicle, which directly affects its handling performance, driving comfort, and driving safety. The dynamics of the 8-DoF full-vehicle suspension with seat active control are established based on rigid-body dynamics, and the time-domain stochastic excitation model of four tires is constructed by the filtered white noise method. The suspension dynamics model and road surface model are constructed on the Matlab/Simulink simulation software platform, and the simulation study of the dynamic characteristics of active suspension based on the fractional-order PIλDµ control strategy is carried out. The three performance indicators of acceleration, suspension dynamic deflection, and tire dynamic displacement are selected to construct the fitness function of the genetic algorithm, and the structural parameters of the fractional-order PIlDm controller are optimized using the genetic algorithm. The control effect of the optimized fractional-order PIlDm controller based on the genetic algorithm is analyzed by comparing the integer-order PID control suspension and passive suspension. The simulation results show that for optimized fractional-order PID control suspension, compared with passive suspension, the average optimization of the root mean square (RMS) of acceleration under random road conditions reaches over 25%, the average optimization of suspension dynamic deflection exceeds 30%, and the average optimization of tire dynamic displacement is 5%. However, compared to the integer-order PID control suspension, the average optimization of the root mean square (RMS) of acceleration under random road conditions decreased by 5%, the average optimization of suspension dynamic deflection increased by 3%, and the average optimization of tire dynamic displacement increased by 2%.

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Section: Comparison With the Existing Literaturementioning

confidence: 99%

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

Yin,

Wang,

et al. 2024

WEVJ

View full text Add to dashboard Cite

show abstract

“…Besides, the Fuzzy control method is also commonly used for active suspension models [46]. The logic modes of this method can respond well to external stimuli [47,48]. Besides, many other intelligent control methods have also been applied to the active suspension system [49][50][51][52][53].…”

Section: Literature Reviewmentioning

confidence: 99%

LQR Control with the New Triple In-Loops Algorithm for Optimization of the Tuning Parameters

Nguyen¹,

Nguyen²,

Dang³

et al. 2022

MMEP

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The suspension system plays a role in ensuring the stability of the vehicle when traveling on the road. On many modern vehicles, the active suspension system has been proposed to replace the conventional passive suspension system. The performance of the controller for the active suspension system depends on its control method. In this paper, a half dynamics model of the vehicle is established. Besides, the LQR control method is also used. The parameters of the control matrix are calculated through the triple in-loop optimization algorithm, which has been shown in the research. This is a completely novel algorithm. This algorithm helps to choose the most optimal parameters. Thus, it ensures the efficiency and stability of the controller. The calculation and comparison process are done automatically. When the loop ends, the optimal parameters are explicitly indicated. The simulation process is done in the MATLAB-Simulink environment. The results of the research showed that when the LQR controller, which was optimized through the triple in-loop algorithm used, the vehicle's oscillation was significantly reduced. In the three survey situations, the values of the roll angle and the angular acceleration of the sprung mass are guaranteed to be stable. Besides, when using this controller, the phenomenon of “chattering” after the excitation ends does not appear. This topic can be further developed in the future.

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Pitch stability control of variable wheelbase 6WID unmanned ground vehicle considering tire slip energy loss and energy-saving suspension control

Chen

Jiang

Tang

et al. 2023

Energy

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Improvement of ride performance with an active suspension based on fuzzy logic control

Cited by 4 publications

References 16 publications

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

LQR Control with the New Triple In-Loops Algorithm for Optimization of the Tuning Parameters

Pitch stability control of variable wheelbase 6WID unmanned ground vehicle considering tire slip energy loss and energy-saving suspension control

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