Experimental study of road identification by LSTM with application to adaptive suspension damping control

Liang, Guanqun; Zhao, Tongtiegang; Shangguan, Zhengwei; Li, Ningfei; Wu, Mingyu; Lyu, Jingcheng; Du, Yongchang; Wei, Yintao

doi:10.1016/j.ymssp.2022.109197

Cited by 18 publications

(6 citation statements)

References 23 publications

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“…where K E is the counter-electromotive force constant, K T is the torque constant, J is the total rotational inertia of the working mechanical system converted to the motor shaft, and T d is the load torque. It is known that the angular velocity of motor shaft rotation ω = dθ/dt, and the mathematical model equation of the DC motor can be obtained from the above Equations ( 12)- (15).…”

Section: Motor Mathematical Modelmentioning

confidence: 99%

“…The results show that the proposed controller is advantageous. Liang et al [ 15 ] used a long short-term memory (LSTM) network to recognize road information, and the CDC (continuous damper) was controlled according to the road recognition information to realize adaptive damping switching control. The optimal damping coefficient was calculated after testing on different roads.…”

Section: Introductionmentioning

confidence: 99%

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Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control

Hu,

Liu,

Wang

et al. 2024

Sensors

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In this study, an electric oil and gas actuator based on fractional-order PID position feedback control is proposed, through which the damping coefficient of the suspension system is adjusted to realize the active control of the suspension. An FOPID algorithm is used to control the motor’s rotational angle to realize the damping adjustment of the suspension system. In this process, the road roughness is collected by the sensors as the criterion of damping adjustment, and the particle swarm algorithm is utilized to find the optimal objective function under different road surface slopes, to obtain the optimal cornering value. According to the mathematical and physical model of the suspension system, the simulation model and the corresponding test platform of this type of suspension system are built. The simulation and experimental results show that the simulation results of the fractional-order nonlinear suspension model are closer to the actual experimental values than those of the traditional linear suspension model, and the accuracy of each performance index is improved by more than 18.5%. The designed active suspension system optimizes the body acceleration, suspension dynamic deflection, and tire dynamic load to 89.8%, 56.7%, and 73.4% of the passive suspension, respectively. It is worth noting that, compared to traditional PID control circuits, the FOPID control circuit designed for motors has an improved control performance. This study provides an effective theoretical and empirical basis for the control and optimization of fractional-order nonlinear suspension systems.

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Section: Motor Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control

Hu,

Liu,

Wang

et al. 2024

Sensors

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show abstract

“…The placement of vibration sensors on the vehicle can influence the sensitivity to specific road anomalies. Studies have investigated the effectiveness of sensors mounted on dashboards, floorboard, axles, wheels, and the vehicle chassis [10][11][12] . Vibration data often requires preprocessing steps like noise filtering and smoothing techniques to improve its quality and consistency before feeding it into machine learning models.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of data representation techniques for vibration based road surface condition classification

Raslan,

Alrahmawy,

Mohammed

et al. 2024

Sci Rep

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The accurate classification of road surface conditions plays a vital role in ensuring road safety and effective maintenance. Vibration-based techniques have shown promise in this domain, leveraging the unique vibration signatures generated by vehicles to identify different road conditions. In this study, we focus on utilizing vehicle-mounted vibration sensors to collect road surface vibrations and comparing various data representation techniques for classifying road surface conditions into four classes: normal road surface, potholes, bad road surface, and speedbumps. Our experimental results reveal that the combination of multiple data representation techniques results in higher performance, with an average accuracy of 93.4%. This suggests that the integration of deep neural networks and signal processing techniques can produce a high-level representation better suited for challenging multivariate time series classification issues.

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“…Power spectrum analysis and time series model are relatively mature methods for stochastic pavement excitation modeling, and the commonly used methods for simulating pavement roughness include the trigonometric series synthesis method, AR method, Poisson method, filtering white noise method, etc. Aiming at the semi-active suspension model in this paper, the filtered white noise excitation signal of the random road disturbance model was established in MATLAB/Simulink, and the excitation graph at the speed of 30 km/h, 70 km/h, and 120 km/h on B-level road surface was selected as the excitation signal of semiactive suspension (Liang et al, 2022).…”

Section: Random Road Disturbance Modelmentioning

confidence: 99%

Adaptive variable domain fuzzy PID control strategy based on road excitation for semi-active suspension using CDC shock absorber

Zhang

Zhao

Zhang

2023

Journal of Vibration and Control

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Electronic suspensions can take into account the ride comfort and safety of the vehicle, the continuous damping control (CDC) shock absorber is the core component of the electronic suspension. CDC shock absorber and electronic suspension have a promising future for application in automotive. This paper proposed an adaptive variable domain fuzzy PID control strategy for semi-active suspensive to effectively improve the vibration reduction effect of the automobile suspension system. By analyzing the dynamic performance of the semi-active suspension system coupled with the CDC shock absorber to get the control variables, we deduce the math function of semi-active suspension. In addition, the simulation model of the semi-active suspension based on the bench test of shock absorbers was established. Under the observation of performance indicators, though comparing the new control and other different control strategies, which can prove the effectiveness of the new method. From the simulation results, the shock absorber simulation model is correct and the performance of the proposed control strategies are effective than the traditional PID control and fuzzy control under the random road excitation. In particular, in the 120 km/h case by using VAC, the peak values of the suspension dynamic deflection, vehicle body acceleration, and car body dynamic load are reduced by 49.6%, 50%, and 50%. For a semi-active suspension using the CDC shock absorber, the proposed control method provides better ride comfort to passengers due to lower peak compared with the fuzzy control strategy and the PID control strategy, it can be used as an optimized design method for suspension.

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Experimental study of road identification by LSTM with application to adaptive suspension damping control

Cited by 18 publications

References 23 publications

Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control

Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control

Evaluation of data representation techniques for vibration based road surface condition classification

Adaptive variable domain fuzzy PID control strategy based on road excitation for semi-active suspension using CDC shock absorber

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