Coordination Control of Multi-Axis Steering and Active Suspension System for High-Mobility Emergency Rescue Vehicles

Chen, Hao; Gong, Mingde; Zhao, Ding; Liu, Wenbin; Jia, Guan-Yong

doi:10.3390/math10193562

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…Gong et al [84] achieved a 38.90% optimization of AVB 4 (k). Chen et al [88] applied double sliding mode control and recorded 29.08% optimization of AVB 4 (k). Wei et al [94] applied fuzzy PID control on vehicles and achieved an AVB 4 (k) optimization of 3.54%.…”

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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“…At present, in the research of active suspension control, the 1/4 vehicle model, the 1/2 vehicle model, and the whole vehicle model are the research objects [ 24 , 25 , 26 ]. Using the two-degrees-of-freedom model as the object of study can better reflect the problem of vertical vibration but it ignores the mutual coupling between the suspensions and the influence of the angular motion of the body in the pitch and roll directions on the comfort, and the control requirements for vehicle comfort cannot be fully described.…”

Section: Seven-dof Vehicle Modelmentioning

confidence: 99%

Fast Distributed Model Predictive Control Method for Active Suspension Systems

Zhang

Yang

et al. 2023

Sensors

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In order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then, for engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, comfort, and handling stability of the vehicle at the same time.

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“…The reason for selecting the dynamic responses for the step steer test is that the step steer test provides a much harsher driving condition than the slalom and double-lane tests. Therefore, the dynamic responses to the step steer test have been analyzed in many studies [25,26,27]. When the step steer test is performed, the dynamic response to the pitch angle is much smaller than the dynamic response to the bounce displacement and roll angle.…”

Section: Dynamic Responsesmentioning

confidence: 99%

Dynamic Modeling and Analysis of a Driving Passenger Vehicle

Yun¹,

Lee²,

Jang³

et al. 2023

Preprint

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This study presents a dynamic model of a passenger vehicle and analyzes its dynamic characteristics and responses. A dynamic vehicle model with seven degrees of freedom was established to analyze the behavior of a driving vehicle. The vehicle model had three degrees of freedom for the motion of the sprung mass and four degrees of freedom for the unsprung masses. For this model, the equations of motion were derived using Lagrange’s equation. To verify the model, the suspension deformations computed using the model were compared with the deformations measured through three actual vehicle driving tests which are the slalom, double lane change, and step steer tests. Furthermore, we investigated the effects of suspension stiffness, suspension damping, and anti-roll bar torsional stiffness on the dynamic characteristics and responses of the vehicle model. The developed dynamic vehicle model may help vehicle designers predict the dynamic responses of a vehicle through simulation without performing a driving test.

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Coordination Control of Multi-Axis Steering and Active Suspension System for High-Mobility Emergency Rescue Vehicles

Cited by 7 publications

References 32 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

Fast Distributed Model Predictive Control Method for Active Suspension Systems

Dynamic Modeling and Analysis of a Driving Passenger Vehicle

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