Passenger Body Vibration Control in Active Quarter Car Model using Hybrid ANFIS PID Controller

Faridabad,; Haryana,

doi:10.5815/ijisa.2018.05.06

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…[4] and [5] have investigated the use of PID controllers in active suspension systems, highlighting the role of intelligent controllers such as adaptive neural networks and fuzzy logic in enhancing passenger comfort. Additionally, the study by [6] demonstrated the improved performance of PID controllers in active quarter-car models, specifically addressing passenger ride comfort and vehicle handling issues.…”

Section: Introductionmentioning

confidence: 98%

Prediction of the PID Parameters for the Vibration Control with ANN Considering Half-Car Vehicle Model

2023

Proceeding Book of 3rd International Conference on Scientific and Academic Research ICSAR 2023

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This study aims to reduce the rate of change in velocity of a 4-degree-of-freedom half-vehicle model, which includes two wheels and a vehicle body, by implementing a PID controller. Here, the vehicle is subjected to a sine wave with a magnitude of 0.05 m and a span of 40 m. The velocity of the car is measured as 100 kilometers per hour. The study analyzed the vehicle's vertical acceleration while maintaining a fixed speed and using a specific sine wavelength. In addition, the PID coefficients were optimized using an artificial neural network.

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Section: Introductionmentioning

confidence: 98%

Prediction of the PID Parameters for the Vibration Control with ANN Considering Half-Car Vehicle Model

2023

Proceeding Book of 3rd International Conference on Scientific and Academic Research ICSAR 2023

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“…Simulations show the effectiveness of the NN compared to all these systems. A quarter car model with passenger body and seat was developed by [9] to capture the dynamic behavior of a car to improve ride comfort and safety. Two controllers, which are Adaptive Neuro-Fuzzy Inference System (ANFIS) and Hybrid ANFIS PID (HANFISPID) was developed by [9].…”

Section: Introductionmentioning

confidence: 99%

“…A quarter car model with passenger body and seat was developed by [9] to capture the dynamic behavior of a car to improve ride comfort and safety. Two controllers, which are Adaptive Neuro-Fuzzy Inference System (ANFIS) and Hybrid ANFIS PID (HANFISPID) was developed by [9]. Results demonstrate that the proposed HANFISPID control plan achieves better ride comfort and safety of travel compared to the passive system and ANFIS controller.…”

Section: Introductionmentioning

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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Suspension of Heavy Trucks with Intelligent Control Using Artificial Neural Networks Particle Swarm Optimization (ANN-PSO)

Hamza

Dridi

Bousnina

et al. 2023

Applied Condition Monitoring

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Passenger Body Vibration Control in Active Quarter Car Model using Hybrid ANFIS PID Controller

Cited by 3 publications

References 20 publications

Prediction of the PID Parameters for the Vibration Control with ANN Considering Half-Car Vehicle Model

Prediction of the PID Parameters for the Vibration Control with ANN Considering Half-Car Vehicle Model

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

Suspension of Heavy Trucks with Intelligent Control Using Artificial Neural Networks Particle Swarm Optimization (ANN-PSO)

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