Simulation of Electrical Actuator and Air Spring Actuator Controlled Suspension Systems for Automotive Vehicles

Sathishkumar, P.; Jancirani, J.; John, D.; Arun, B.

doi:10.4273/ijvss.7.3.07

Cited by 2 publications

(2 citation statements)

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“…Simulation results showed that the proposed control strategy can effectively adjust parameters based on changing road conditions. Sathishkumar [7] used a fuzzy control strategy to actively control the three-degrees-of-freedom air suspension seat model of the 1/4 vehicle. The vehicle body acceleration and velocity were used as inputs to the controller, and the acceleration of the seat and the output of the vehicle body were analyzed to evaluate the performance of the active suspension system.…”

Section: Introductionmentioning

confidence: 99%

A Vibration Control Method Using MRASSA for 1/4 Semi-Active Suspension Systems

et al. 2023

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The multi-subpopulation refracted adaptive salp swarm algorithm (MRASSA) was proposed for vibration control in 1/4 semi-active suspension systems. The MRASSA algorithm was applied to optimize suspension damping performance by addressing the local optimal and slow convergence speed challenge of the standard salp swarm algorithm for two-degrees-of-freedom 1/4 semi-active suspension systems. The developed MRASSA contains three key improvements: (1) partitioning multi-subpopulation; (2) applying refracted opposition-based learning; (3) adopting adaptive factors. In order to verify the performance of the MRASSA approach, a 1/4 suspension Simulink model was developed for simulation experiments. To further validate the results, a physical platform was built to test the applicability of the simulation model. The optimized suspension performance of MRASSA was also compared with three optimized models, namely, standard SSA, Single-Objective Firefly (SOFA) and Whale-optimized Fuzzy-fractional Order (WOAFFO). The experimental results showed that MRASSA outperformed the other models, achieving better suspension performance in complex environments such as a random road with a speed of 60 km/h. Compared to passive suspension, MRASSA led to a 41.15% reduction in sprung mass acceleration and a 15–25% reduction compared to other models. Additionally, MRASSA had a maximum 20% reduction in suspension dynamic deflection and dynamic load. MRASSA also demonstrated a faster convergence speed, finding the optimal solution faster than the other algorithms. These results indicate that MRASSA is superior to other models and has potential as a valuable tool for suspension performance optimization.

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Section: Introductionmentioning

confidence: 99%

A Vibration Control Method Using MRASSA for 1/4 Semi-Active Suspension Systems

et al. 2023

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“…25 Sathishkumar used a fuzzy control strategy to actively control the 3-DOF air suspension system model of 1/4 vehicle. 26 In order to analyze the performance of the active suspension system, the seat and body acceleration were selected to judge the state of the control system. At the same time, the semi-active air suspension system of light vehicle was analyzed by fuzzy and PID control strategies.…”

Section: Introductionmentioning

confidence: 99%

Research on nonlinear model and fuzzy fractional order PI^λD^μ control of air suspension system

Wang

et al. 2021

Journal of Low Frequency Noise, Vibration and Active Control

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To improve the ride comfort of wheeled armored vehicles, air springs are used. To describe the vehicle motion more accurately, a nine-degree-of-freedom air suspension system for the whole vehicle was established, and its equations of motion were derived. Through theoretical analysis of the stiffness characteristics and forces on the air springs, the nonlinear restoring force was obtained as a cubic polynomial of the air spring displacement. The simulation results obtained by fitting the polynomial and radial basis function curves with MATLAB/Simulink software are consistent with the actual test results, thus verifying the correctness of the nonlinear air spring polynomial model. Finally, a fuzzy fractional order PIλDμ controller is designed and simulated for the vehicle-seat-body model in terms of wheel dynamic load, suspension dynamic deflection, body acceleration, and other indicators. The simulation results show that the fuzzy fractional order PIλDμ Proportion Integral Differential (PID) control strategy has better overall performance than the PID control strategy, fuzzy control strategy, and fuzzy PID control strategy.

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Simulation of Electrical Actuator and Air Spring Actuator Controlled Suspension Systems for Automotive Vehicles

Cited by 2 publications

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A Vibration Control Method Using MRASSA for 1/4 Semi-Active Suspension Systems

A Vibration Control Method Using MRASSA for 1/4 Semi-Active Suspension Systems

Research on nonlinear model and fuzzy fractional order PI^λD^μ control of air suspension system

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Simulation of Electrical Actuator and Air Spring Actuator Controlled Suspension Systems for Automotive Vehicles

Cited by 2 publications

References 0 publications

A Vibration Control Method Using MRASSA for 1/4 Semi-Active Suspension Systems

A Vibration Control Method Using MRASSA for 1/4 Semi-Active Suspension Systems

Research on nonlinear model and fuzzy fractional order PIλDμ control of air suspension system

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Research on nonlinear model and fuzzy fractional order PI^λD^μ control of air suspension system