Study on Multi-Mode Switching Control Strategy of Active Suspension Based on Road Estimation

Liu, Jianze; Liu, Jiang; Li, Yang; Wang, Guangzheng; Yang, Fuzi

doi:10.3390/s23063310

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…In this paper, a fractional-order nonlinear suspension system model is constructed mainly based on the viscoelastic properties of the employed adjustable-damped oil–air actuator. The constructed active model of the 1/4 vehicle suspension system [ 28 ] is shown in Figure 3 below. In this figure,

denotes the elastic force output from the nonlinear elastic element,

represents the sprung load mass,

represents the unsprung load mass,

is the tire stiffness, and q represents the road excitation.…”

Section: Principle and Modelingmentioning

confidence: 99%

Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control

Hu,

Liu,

Wang

et al. 2024

Sensors

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In this study, an electric oil and gas actuator based on fractional-order PID position feedback control is proposed, through which the damping coefficient of the suspension system is adjusted to realize the active control of the suspension. An FOPID algorithm is used to control the motor’s rotational angle to realize the damping adjustment of the suspension system. In this process, the road roughness is collected by the sensors as the criterion of damping adjustment, and the particle swarm algorithm is utilized to find the optimal objective function under different road surface slopes, to obtain the optimal cornering value. According to the mathematical and physical model of the suspension system, the simulation model and the corresponding test platform of this type of suspension system are built. The simulation and experimental results show that the simulation results of the fractional-order nonlinear suspension model are closer to the actual experimental values than those of the traditional linear suspension model, and the accuracy of each performance index is improved by more than 18.5%. The designed active suspension system optimizes the body acceleration, suspension dynamic deflection, and tire dynamic load to 89.8%, 56.7%, and 73.4% of the passive suspension, respectively. It is worth noting that, compared to traditional PID control circuits, the FOPID control circuit designed for motors has an improved control performance. This study provides an effective theoretical and empirical basis for the control and optimization of fractional-order nonlinear suspension systems.

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denotes the elastic force output from the nonlinear elastic element,

represents the sprung load mass,

represents the unsprung load mass,

is the tire stiffness, and q represents the road excitation.…”

Section: Principle and Modelingmentioning

confidence: 99%

Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control

Hu,

Liu,

Wang

et al. 2024

Sensors

Self Cite

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show abstract

“…It is necessary to determine the specific parameters of the vehicle, take the three ride performance evaluation indexes of the passive suspension as the baseline before optimization, and optimize the ratio between the output performance indexes of the LQR controlled suspension and the passive suspension by the improved algorithm as the fitness function of the optimization model. Therefore, the fitness function of the system is designed as follows [23]:…”

Section: Adaptation Function Designmentioning

confidence: 99%

Active suspension LQR control based on modified differential evolutionary algorithm optimization

Zou,

Zuo

2024

J. vibroeng.

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The selection of weight matrices Q and R in the LQR control strategy for active suspension is susceptible to subjective interference. To address this issue, a modified differential evolutionary algorithm is proposed to optimize the active suspension LQR controller, ensuring that the weighting coefficients are set to their optimal values. The differential evolutionary algorithm exhibits drawbacks in terms of its slow convergence rate and the significant impact of algorithm parameter settings on the obtained results. An modified differential evolutionary algorithm that is adaptive to the two candidate mutation strategies and adaptively adjusts the scaling factor and crossover rate is proposed so as to better improve the ability of jumping out of the local optimum and global search. The algorithm's functionality is verified by constructing a 1/4 suspension model in the Simulink software platform and implementing a modified differential evolution algorithm program written in C++ language using MATLAB. The program iterates through Simulink inputs to obtain the optimal fitness value for three suspension comfort indices. By comparing the results with those obtained from passive suspension and traditional LQR control of active suspension, optimizing the LQR control of active suspension based on the modified differential evolution algorithm can effectively reduce vehicle vibration amplitude while considering overall suspension performance enhancement, thereby significantly improving ride comfort and handling stability.

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“…In this paper, a fractional-order nonlinear suspension system model is constructed mainly based on the viscoelastic properties of the employed adjustable-damped oil-air actuator. The constructed active model of the 1/4 vehicle suspension system [28] is shown in Figure 3 below. In this figure, F k denotes the elastic force output from the nonlinear elastic element, m 2 represents the sprung load mass, m 1 represents the unsprung load mass, k 1 is the tire stiffness, and q represents the road excitation.…”

Section: Suspension System Modelingmentioning

confidence: 99%

Research on Electric Oil-Pneumatic Active Suspension Based on Fractional Order PID Position Control

Hu,

Liu,

Wang

et al. 2024

Preprint

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Due to the nonlinear stiffness and fractional-order damping characteristics of some real suspension systems, coupled with the presence of viscoelastic materials, fractional-order nonlinear suspension system modeling has become a focus of research in recent years. In this study, an electric oil and gas actuator based on fractional-order PID position feedback control is proposed, through which the damping coefficient of the suspension system is adjusted to realize the active control of the suspension. A fractional order PID algorithm is used to control the motor's rotational angle to realize the damping adjustment of the suspension system. In this process, the road roughness is collected by the sensors as the criterion of damping adjustment, and the particle swarm algorithm is utilized to find the optimal objective function under different road surface slopes, to obtain the optimal cornering value. According to the mathematical and physical model of the suspension system, the simulation model and the corresponding test platform of this type of suspension system are built. The simulation and experimental results show that the simulation results of the fractional-order nonlinear suspension model are closer to the actual experimental values than those of the traditional linear suspension model, and the accuracy of each performance index is improved by more than 18.5%. The designed active suspension system optimizes the body acceleration, suspension dynamic deflection, and tire dynamic load to 89.8%, 56.7%, and 73.4% of the passive suspension, respectively. Notably, the designed fractional order PID control circuit for the motor shows better control results compared to the conventional PID control circuit. This study provides an effective theoretical and empirical basis for the control and optimization of fractional-order nonlinear suspension systems.

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Study on Multi-Mode Switching Control Strategy of Active Suspension Based on Road Estimation

Cited by 7 publications

References 42 publications

Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control

Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control

Active suspension LQR control based on modified differential evolutionary algorithm optimization

Research on Electric Oil-Pneumatic Active Suspension Based on Fractional Order PID Position Control

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