Off-Road Vehicle Seat Suspension Optimisation, Part II: Comparative Study between Meta-Heuristic Optimisation Algorithms

Gohari, Mohammad; Tahmasebi, Mona

doi:10.1260/0263-0923.33.4.443

Cited by 11 publications

(13 citation statements)

References 29 publications

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“…In recent years, the use of these techniques has been expanding. [12][13][14][15] However, not many studies have been conducted into the effects of biofuels on the noise pollution emitted by tractors. Furthermore, because of the importance of farmers' health, it seems that conducting some comprehensive research in this respect is of the essence.…”

Section: Introductionmentioning

confidence: 99%

An analysis of noise pollution emitted by moving MF285 Tractor using different mixtures of biodiesel, bioethanol and diesel through artificial intelligence

Ghaderi

Javadikia

Naderloo

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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In the present study, the noise pollution from different compositions of biodiesel, bioethanol, and diesel fuels in MF285 Tractor was studied in the second and third gears from two positions: driver and bystander, at 1000 and 1600 r/min, and running on 10 different fuel levels. For data analysis, the ANFIS network, neural network, and response surface methodology were applied. Comparing the means of noise pollution at different levels demonstrated that the B 25 E 6 D 69 fuel, made up of 25% biodiesel and 6% bioethanol, had the lowest noise pollution. The lowest noise pollution was at 1000 r/ min. Although the noise pollution emitted in the third gear was a little more than that emitted in the second gear. All the resultant models, laid by response surface methodology, neural network, and ANFIS had excellent results. Considering the statistical criteria, the best models with high correlation coefficients and low mean square errors were ANFIS, response surface methodology, and artificial neural network models, respectively.

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Section: Introductionmentioning

confidence: 99%

An analysis of noise pollution emitted by moving MF285 Tractor using different mixtures of biodiesel, bioethanol and diesel through artificial intelligence

Ghaderi

Javadikia

Naderloo

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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show abstract

“…Gohari and Tahmasebi [24] had presented optimization of seat suspension of off-road vehicle using three algorithm namely genetic algorithm, particle swam optimization and harmony search algorithm. An ANN bionic model based on experimental data is used in this study.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

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

Nagarkar¹,

El-Gohary

Bhalerao

et al. 2019

SN Appl. Sci.

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This paper presents the modeling and optimization of quarter car suspension system using Macpherson strut. A mathematical model of quarter car is developed, simulated and optimized in Matlab/Simulink ® environment. The results are validated using test rig. The suspension system parameters are optimized using a genetic algorithm for objective functions viz. vibration dose value (VDV), frequency weighted root mean square acceleration (hereafter called as RMS acceleration), maximum transient vibration value, root mean square suspension space and root mean square tyre deflection. ISO 2631-1 standard is adopted to assess ride and health criterion. Results shows that optimum parameters provide ride comfort and health criterions over classical design. The optimization results are experimentally validated using quarter car test setup. The genetic algorithm optimization results are further extended to the artificial neural network simulation and prediction model. Artificial neural network model is carried out in Matlab/Simulink ® environment and Neuro Dimensions. Simulation, experimental and predicted results are in close correlation. The optimized system reduces the values of VDV by 45%. Also, RMS acceleration is reduced by 47%. Thus, the optimized system improved ride comfort by reducing RMS acceleration and improved health criterion by reducing the VDV. Finally ANN can be used for predicting the optimum suspension parameters values with good agreement.

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“…5. For each particle perform the following actions: If the fitness of the newly computed particle is better than its personal best location, then replace the personal best particle's location with those of the newly computed particle as described by equation (38). 6.…”

Section: Pso Algorithmmentioning

confidence: 99%

Proportional + integral + derivative control of nonlinear full-car electrohydraulic suspensions using global and evolutionary optimization techniques

Dahunsi

Dangor

Pedro

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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Resolving the trade-offs between suspension travel, ride comfort, road holding, vehicle handling and power consumption is the primary challenge in the design of active vehicle suspension system. Multi-loop proportional + integral + derivative controllers’ gains tuning with global and evolutionary optimization techniques is proposed to realize the best compromise between these conflicting criteria for a nonlinear full-car electrohydraulic active vehicle suspension system. Global and evolutionary optimization methods adopted include: controlled random search, differential evolution, particle swarm optimization, modified particle swarm optimization and modified controlled random search. The most improved performance was achieved with the differential evolution algorithm. The modified particle swarm optimization and modified controlled random search algorithms performed better than their predecessors, with modified controlled random search performing better than modified particle swarm optimization in all aspects of performance investigated both in time and frequency domain analyses.

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Off-Road Vehicle Seat Suspension Optimisation, Part II: Comparative Study between Meta-Heuristic Optimisation Algorithms

Cited by 11 publications

References 29 publications

An analysis of noise pollution emitted by moving MF285 Tractor using different mixtures of biodiesel, bioethanol and diesel through artificial intelligence

An analysis of noise pollution emitted by moving MF285 Tractor using different mixtures of biodiesel, bioethanol and diesel through artificial intelligence

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

Proportional + integral + derivative control of nonlinear full-car electrohydraulic suspensions using global and evolutionary optimization techniques

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