Anti-Jerk Optimal Preview Control Strategy to Enhance Performance of Active and Semi-Active Suspension Systems

Youn, Iljoong; Ahmad, Ejaz

doi:10.3390/electronics11101657

Cited by 8 publications

(3 citation statements)

References 33 publications

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“…Hence, such significant improvements in ride quality can be achievable at the penalty of vehicle handling. In [ 42 ], using a semi-active suspension system, we have implemented a preview-based anti-jerk control strategy to improve ride comfort without degrading the road-holding capability under different road conditions. Despite the exciting results, to the researcher’s knowledge, the previous anti-jerk methodologies only focus on enhancing ride comfort using conventional active or semi-active suspension systems.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement during Attitude Motion of a Vehicle Using Aerodynamic-Surface-Based Anti-Jerk Predictive Controller

Ahmad

Youn

2023

Sensors

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This study presents the effectiveness of an anti-jerk predictive controller (AJPC) based on active aerodynamic surfaces to handle upcoming road maneuvers and enhance vehicle ride quality by mitigating external jerks operating on the body of the vehicle. In order to eliminate body jerk and improve ride comfort and road holding during turning, accelerating, or braking, the proposed control approach assists the vehicle in tracking the desired attitude position and achieving a realistic operation of the active aerodynamic surface. Vehicle speed and upcoming road data are used to calculate the desired attitude (roll or pitch) angles. The simulation results are performed for AJPC and predictive control strategies without jerk using MATLAB. The simulation results and comparison based on root-mean-square (rms) values show that compared to the predictive control strategy without jerk, the proposed control strategy significantly reduces the effects of vehicle body jerks transmitted to the passengers, improving ride comfort without degrading vehicle handling at the cost of slow desired angle tracking.

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Section: Introductionmentioning

confidence: 99%

Performance Improvement during Attitude Motion of a Vehicle Using Aerodynamic-Surface-Based Anti-Jerk Predictive Controller

Ahmad

Youn

2023

Sensors

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“…Experimental results showed that the spring mass acceleration (SMA) of MFRR is much smaller than that of the passive suspension system (PS) and FPID on single-bump and sine wave roads, and the control system exhibits good control performance. Youn [12] controlled the root mean square of acceleration of 1/4 suspension through road prediction simulation and optimal control theory. Time domain simulation shows that the preview control strategy can effectively reduce suspension vibration.…”

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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“…Young I. [29] et al designed an anti-jerk optimal predictive control method for active and semi-active suspension systems. This approach can reduce body jerk and improve ride comfort.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Stability Analysis under Extreme Operating Conditions Based on LQR Control

Zhou

Bao³

et al. 2022

Sensors

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Under extreme working conditions such as high-speed driving on roads with a large road surface unevenness coefficient, turning on a road with a low road surface adhesion coefficient, and emergency acceleration and braking, a vehicle’s stability deteriorates sharply and reduces ride comfort. There is extensive existing research on vehicle active suspension control, trajectory tracking, and control methods. However, most of these studies focus on conventional operating conditions, while vehicle stability analysis under extreme operating conditions is much less studied. In order to improve the stability of the whole vehicle under extreme operating conditions, this paper investigates the stability of a vehicle under extreme operating conditions based on linear quadratic regulator (LQR) control. First, a seven degrees of freedom (7-DOF) dynamics model of the whole vehicle is established based on the use of electromagnetic active suspension, and then an LQR controller of the electromagnetic active suspension is designed. A joint simulation platform incorporating MATLAB and CarSim was built, and the CarSim model is verified by real vehicle tests. Finally, the stability of the vehicle under four different ultimate operating conditions was analyzed. The simulation results show that the root mean square (RMS) values of body droop acceleration and pitch angle acceleration are improved by 57.48% and 28.81%, respectively, under high-speed driving conditions on Class C roads. Under the double-shift condition with a low adhesion coefficient, the RMS values of body droop acceleration, pitch acceleration, and roll angle acceleration are improved by 58.25%, 55.41%, and 31.39%, respectively. These results indicate that electromagnetic active suspension can significantly improve vehicle stability and reduce driving risk under extreme working conditions when combined with an LQR controller.

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Anti-Jerk Optimal Preview Control Strategy to Enhance Performance of Active and Semi-Active Suspension Systems

Cited by 8 publications

References 33 publications

Performance Improvement during Attitude Motion of a Vehicle Using Aerodynamic-Surface-Based Anti-Jerk Predictive Controller

Performance Improvement during Attitude Motion of a Vehicle Using Aerodynamic-Surface-Based Anti-Jerk Predictive Controller

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

Vehicle Stability Analysis under Extreme Operating Conditions Based on LQR Control

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