Artificial neural network predication and validation of optimum suspension parameters of a passive suspension system

Nagarkar, Mahesh; El-Gohary, Mohamed A.; Bhalerao, Yogesh; Patil, G. J. Vikhe; Patil, Rahul N. Zaware

doi:10.1007/s42452-019-0550-0

Cited by 17 publications

(10 citation statements)

References 22 publications

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“…Figure 23, which shows the CPSD plot of the acceleration, reveals that compared to PSO-tuned LQR, the proposed approach offers a significant reduction in vertical acceleration in both low-and high-frequency spectra, thereby considerably increasing passenger comfort. Moreover, to quantify the performance of the controller for both the road profiles considered for evaluation, we compute three performance measures based on the ISO 2361-1 standards, which provide a guide to evaluate the human exposure to whole-body vibrations [31,32]. e three performance metrics considered are root means square (RMS) of suspension travel, frequency-weighted RMS (FWRMS) of acceleration, and vibration dose value (VDV), which are mathematically described as follows: , (17)…”

Section: Resultsmentioning

confidence: 99%

Hardware in the Loop Testing of Adaptive Inertia Weight PSO-Tuned LQR Applied to Vehicle Suspension Control

Reddipogu

Elumalai

2020

Journal of Control Science and Engineering

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This paper presents an adaptive inertia weight particle swarm optimization (AIWPSO) employed for solving the multiobjective weight optimization problem of LQR applied for the vehicle active suspension system (ASS). To meet the competing control objectives of ASS including the ride comfort, road handling, and suspension travel, the state feedback controller design for ASS is formulated as an optimization problem and an improved PSO is employed for finding the optimal weights of the linear-quadratic regulator (LQR). Specifically, for solving the premature convergence of the particles and imbalance between exploration and exploitation capabilities of PSO, an adaptive inertia weight that updates the velocity of the particles based on the success rate is used. The efficacy of the AIWPSO-tuned LQR is experimentally tested on a quarter-car ASS plant using the hardware in loop (HIL) testing for an uneven road surface. Experimental results highlight that, compared to conventional PSO-tuned LQR, the proposed scheme can significantly minimize the vehicle body acceleration due to irregular road profile while guaranteeing the minimum tire friction for passenger safety. The ISO 2361-1 standards adopted to evaluate the ride and health criteria substantiate that the proposed scheme reduces the vibration dose value by 25.34% for a bumpy road profile. Moreover, the cumulative power spectral density (CPSD) of vehicle body acceleration assessed in both low- and high-frequency regions manifests the significant improvement in the ride comfort.

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Section: Resultsmentioning

confidence: 99%

Hardware in the Loop Testing of Adaptive Inertia Weight PSO-Tuned LQR Applied to Vehicle Suspension Control

Reddipogu

Elumalai

2020

Journal of Control Science and Engineering

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“…The difference between the real output and the ANN output is another critical error. This error can be minimized by adjusting the interconnecting weight and threshold value in every neuron [40,41].…”

Section: Road Profilementioning

confidence: 99%

Magnetorheological damper voltage control using artificial neural network for optimum vehicle ride comfort

Yakhni

Ali

El-Gohary

2021

JMES

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Suspension system design is an important challenging duty that facing car manufacturers, so the challenge has become to design the best system in terms of providing ride comfort and handling ability under all driving situations. The goal of this paper is to provide assistance in enhancing the effectiveness of the suspension system. A full car model with eight degrees of freedom (DOF) was developed using MATLAB/Simulink. Validation of the Simulink model was obtained. The model was assumed to travel over a speed hump that has a half sine wave shape and amplitude that changing from 0.01 to 0.2 m. The vehicle was moving with variable speeds from 20 to 120 km/h. Magneto Rheological (MR) damper was implanted to the model to study its effect on ride comfort. Artificial Neural Network (ANN) was used to find the optimum voltage value applied to the MR damper, to skip the hump at least displacement. This network uses road profile and the vehicle speed as inputs. A comparison of the results for passive suspension system and model with MR damper, are illustrated. Results show that the MR damper give significant improvements of the vehicle ride performance over the passive suspension system.

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“…Lyapunov method was done to show system stability. Nagarkar et al [30] presented a mathematical model for a quarter car Macpherson suspension system. The model was simulated and optimized using MATLAB-SIMULINK.…”

Section: Kim and Leementioning

confidence: 99%

Neuro-Fuzzy Volume Control for Quarter Car Air-Spring Suspension System

et al. 2021

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One type of vehicle suspension system is the air suspension system. One of the important functions of the suspension system is to reduce vibration transmitted to the vehicle body from road unevenness. Soft suspension results in better ride comfort and worst handling. While stiff suspension results in better handling and worse comfort. This paper investigates the performance of air suspension in terms of passenger ride comfort and vehicle handling. The performance is tested once with Fuzzy logic control and another with Fuzzy control augmented with Neuro-Fuzzy inference system. A mathematical model is developed to describe the air-spring stiffness behavior. The air-spring suspension is merged on two degrees of freedom model. The air pressure is controlled by a Fuzzy controller as done previously in the literature. On the other hand, our approach is to control the volume change by connecting the air-spring volume to two additional unequal volumes through ON-OFF solenoid valves. The solenoid valves are controlled by Adaptive Neuro-Fuzzy Inference System (ANFIS). A single-degree-of-freedom setup was built from which the required training data for the ANFIS controller was carried out. The experimental setup is developed with variable excitation input in terms of frequency and amplitudes. The acceleration of the sprung mass has been measured and stored at different operating conditions and including different air volumes. The stored data is used for training of volume controller. The controller is tested with Sinusoidal excitation then with ISO 8608 road profile. It is found that increasing the air volume reduces the sprung mass acceleration. Also, the results showed that implementing the Neuro-Fuzzy controller with Fuzzy logic control reduces the sprung mass acceleration significantly by an average value that reaches 5 cm/s 2 at frequencies above 1 Hz while maintaining road holding.

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Artificial neural network predication and validation of optimum suspension parameters of a passive suspension system

Cited by 17 publications

References 22 publications

Hardware in the Loop Testing of Adaptive Inertia Weight PSO-Tuned LQR Applied to Vehicle Suspension Control

Hardware in the Loop Testing of Adaptive Inertia Weight PSO-Tuned LQR Applied to Vehicle Suspension Control

Magnetorheological damper voltage control using artificial neural network for optimum vehicle ride comfort

Neuro-Fuzzy Volume Control for Quarter Car Air-Spring Suspension System

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