Experimental research on vehicle active suspension based on time-delay control

Wu, Kaiwei; Ren, Chuanbo; Yang, Nan; Liu, Lin; Yuan, S.Q.; Shao, Sujuan; Sun, Zehao

doi:10.1080/00207179.2023.2201650

Cited by 5 publications

(2 citation statements)

References 49 publications

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“…Chen et al [85] achieved the lowest DTD i (k) optimization, of only 0.3%. Wu et al [86] achieved a 12.16% optimization of DTD i (k). Yang et al [87] achieved a 10.02% DTD i (k) optimization using a ground-hook control strategy.…”

Section: Comparison With the Existing Literaturementioning

confidence: 99%

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

Yin,

Wang,

et al. 2024

WEVJ

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The suspension system is a crucial part of an electric vehicle, which directly affects its handling performance, driving comfort, and driving safety. The dynamics of the 8-DoF full-vehicle suspension with seat active control are established based on rigid-body dynamics, and the time-domain stochastic excitation model of four tires is constructed by the filtered white noise method. The suspension dynamics model and road surface model are constructed on the Matlab/Simulink simulation software platform, and the simulation study of the dynamic characteristics of active suspension based on the fractional-order PIλDµ control strategy is carried out. The three performance indicators of acceleration, suspension dynamic deflection, and tire dynamic displacement are selected to construct the fitness function of the genetic algorithm, and the structural parameters of the fractional-order PIlDm controller are optimized using the genetic algorithm. The control effect of the optimized fractional-order PIlDm controller based on the genetic algorithm is analyzed by comparing the integer-order PID control suspension and passive suspension. The simulation results show that for optimized fractional-order PID control suspension, compared with passive suspension, the average optimization of the root mean square (RMS) of acceleration under random road conditions reaches over 25%, the average optimization of suspension dynamic deflection exceeds 30%, and the average optimization of tire dynamic displacement is 5%. However, compared to the integer-order PID control suspension, the average optimization of the root mean square (RMS) of acceleration under random road conditions decreased by 5%, the average optimization of suspension dynamic deflection increased by 3%, and the average optimization of tire dynamic displacement increased by 2%.

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Section: Comparison With the Existing Literaturementioning

confidence: 99%

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

Yin,

Wang,

et al. 2024

WEVJ

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show abstract

“…In addition, Kim et al [ 22 ] combined perception technology and proposed a model predictive control of a semi-active suspension with a shift delay compensation using preview road information. Wu et al [ 23 ] proposed a time-delay control strategy and the idea of using a linear motor as the actuator. According to this idea, they proposed a linear equivalent excitation method to optimize the optimal time-delay control parameters under complex excitation.…”

Section: Introductionmentioning

confidence: 99%

Research on Deep Reinforcement Learning Control Algorithm for Active Suspension Considering Uncertain Time Delay

Wang,

Zhao

et al. 2023

Sensors

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The uncertain delay characteristic of actuators is a critical factor that affects the control effectiveness of the active suspension system. Therefore, it is crucial to develop a control algorithm that takes into account this uncertain delay in order to ensure stable control performance. This study presents a novel active suspension control algorithm based on deep reinforcement learning (DRL) that specifically addresses the issue of uncertain delay. In this approach, a twin-delayed deep deterministic policy gradient (TD3) algorithm with system delay is employed to obtain the optimal control policy by iteratively solving the dynamic model of the active suspension system, considering the delay. Furthermore, three different operating conditions were designed for simulation to evaluate the control performance: deterministic delay, semi-regular delay, and uncertain delay. The experimental results demonstrate that the proposed algorithm achieves excellent control performance under various operating conditions. Compared to passive suspension, the optimization of body vertical acceleration is improved by more than 30%, and the proposed algorithm effectively mitigates body vibration in the low frequency range. It consistently maintains a more than 30% improvement in ride comfort optimization even under the most severe operating conditions and at different speeds, demonstrating the algorithm’s potential for practical application.

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Performance of active control in a vehicle seat under random road excitations

Colpo,

Gomes

2024

Int. J. Dynam. Control

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Experimental research on vehicle active suspension based on time-delay control

Cited by 5 publications

References 49 publications

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

Research on Deep Reinforcement Learning Control Algorithm for Active Suspension Considering Uncertain Time Delay

Performance of active control in a vehicle seat under random road excitations

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