Experimental study on shock control of a vehicle semi-active suspension with magneto-rheological damper

Du, Xiumei; Yu, Miao; Fu, Jie; Huang, Chaoqun

doi:10.1088/1361-665x/ab859e

Cited by 33 publications

(17 citation statements)

References 31 publications

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“…This advantage is currently only available at the price of using external power to provide the electromagnetic coils with the required electrical current [ 22 , 23 ]. Damping power in semi-active suspension uses from 5% to 30% of the total damping power to control the process [ 24 , 25 ]. The semi-active damping process creates the possibility of collecting energy from damping rather than simply dissipating it.…”

Section: Introductionmentioning

confidence: 99%

An Automotive Ferrofluidic Electromagnetic System for Energy Harvesting and Adaptive Damping

Lenkutis

Viržonis

Čerškus

et al. 2022

Sensors

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Vibration energy harvesting is receiving significant interest due to the possibility of using extra power in various machines and constructions. This paper presents an energy-harvesting system that has a structure similar to that of a linear generator but uses permanent magnets and magnetorheological fluid insets. The application of a standard vehicle example with low frequencies and amplitudes of the excitations was used for the optimization and experimental runs. The optimization for low excitation amplitudes shows that the best magnetic field change along the slider is obtained using differentially orientated radial magnets of 5 mm in width. This configuration was used for the experimental research, resulting in 1.2–3.28 W of power generated in the coils. The power conditioning system in the experimental research was replaced by loading resistors. Nevertheless, the initial idea of energy harvesting and a damping effect was confirmed by the circuit voltage output.

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Section: Introductionmentioning

confidence: 99%

An Automotive Ferrofluidic Electromagnetic System for Energy Harvesting and Adaptive Damping

Lenkutis

Viržonis

Čerškus

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In Ma et al (2021), a new neuro-inverse model of the MR fluid damper based on the Elman neural network was established, and an optimized fuzzy skyhook controller with gray wolf optimizer algorithm was designed based on the inverse model to control the suspension system. In Du et al (2020), an adaptive skyhook controller based on an improved genetic algorithm was proposed, and the designed controller was verified by the road test for the vehicle installed with MR dampers.…”

Section: Introductionmentioning

confidence: 99%

On an enhanced back propagation neural network control of vehicle semi-active suspension with a magnetorheological damper

Wang

Pang

Luo

et al. 2022

Transactions of the Institute of Measurement and Control

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To improve the ride quality of a vehicle, an enhanced vibration control method is presented for semi-active suspension (SAS) with magnetorheological (MR) damper by combining back propagation neural network (BPNN) and particle swarm optimization (PSO). Based on the test data of MR damper, a non-parametric model of MR damper using adaptive neuro-fuzzy inference system (ANFIS) is first established, and based on that, a dynamics model of the SAS system is derived. Next, a BPNN controller is designed to fulfill the effective control of the current in MR damper. Meanwhile, the improved PSO with adaptive weight and dynamic acceleration constant is introduced to optimize the weights and thresholds of the BPNN controller, which can avoid the designed BPNN falling into the local optimum and then improve the convergence rate of the designed controller. Besides, the stability of the developed controller is analyzed via Lyapunov stability theory. Different from the existing models and methods, the established model can well describe the dynamics behaviors of the actual MR damper, and the proposed control method has better adaptability, convergence speed and precision. Finally, a simulative investigation is performed to validate the effectiveness and feasibility of the proposed controller, compared to existing BP-PID control and the passive suspension, the vehicle acceleration of SAS with this proposed controller is respectively improved by 10% and 30%.

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“…It has great promise for semi-active vibration control [5], such as intelligent suspension damping for automobiles [6], and bridge and building damping [7]. Affected by the internal parameters of the damper and other factors, the dynamic response has a time delay and affects the control accuracy, and the damping performance of the system cannot be guaranteed [8]. After analyzing the dynamic characteristics of the MRD, it can be observed that the link from the driver drive voltage to the damper current has a great influence on the dynamic characteristics of the damper [9].…”

Section: Introductionmentioning

confidence: 99%

Research on Current Drive System of Magnetorheological Damper Based on Fuzzy PI Control

Liang

Xia

et al. 2022

Materials

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Magnetorheological dampers (MRD) are increasingly used in smart structural damping systems due to their good damping properties. In practical applications, as a nonlinear device, the parameters of the internal excitation coil of the magnetorheological damper will change during operation under the influence of the temperature and external environment, deteriorating the dynamic performance of the output current of the driver and reducing the damping effect of the system. Therefore, the current driver needs to be optimized for this phenomenon in order to ensure accurate current output. In this paper, a mathematical model of the buck circuit combined with the MRD equivalent circuit is established, and after analyzing the model, the parameters of the PI controller are rectified to lay the foundation for the design of the adaptive law. Then, with the help of the fuzzy control method, a fuzzy PI control strategy for MRD current driver is established, which enables the current driving system to adjust the control parameters adaptively when the MRD parameters change and ensure the accurate driving current output. The experimental results demonstrate that the fuzzy PI control strategy has a stronger robustness in the face of parameter changes of the control object compared with the traditional PI control at a system parameter change rate of 40%.

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Experimental study on shock control of a vehicle semi-active suspension with magneto-rheological damper

Cited by 33 publications

References 31 publications

An Automotive Ferrofluidic Electromagnetic System for Energy Harvesting and Adaptive Damping

An Automotive Ferrofluidic Electromagnetic System for Energy Harvesting and Adaptive Damping

On an enhanced back propagation neural network control of vehicle semi-active suspension with a magnetorheological damper

Research on Current Drive System of Magnetorheological Damper Based on Fuzzy PI Control

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