A semi-active suspension control algorithm for vehicle comprehensive vertical dynamics performance

Nie, Shida; Zhuang, Ye; Liu, Weiping; Chen, Fan

doi:10.1080/00423114.2017.1299871

Cited by 46 publications

(20 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…e input of the l-th neuron in the output layer is net (3) l (k) � 5 i�1 w (3) li O (2) i + θ (3) l , l � 1, 2, 3,…”

Section: Bp-pid Algorithmmentioning

confidence: 99%

“…Due to the limitation of the passive suspension, the handing stability and ride comfort of the vehicle cannot be satisfied at the same time. erefore, the research and application of the active suspension is becoming increasingly popular, which effectively improves the vehicle dynamic performance [1][2][3]. However, the active suspension adds a new problem in terms of vehicle energy consumption while improving its vibration response.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and Simulation of Energy‐Regenerative Active Suspension Based on BP Neural Network PID Control

Liu

Zhang

et al. 2019

Shock and Vibration

View full text Add to dashboard Cite

In this paper, an electromagnetic energy-regenerative suspension system is proposed to achieve active control and vibration energy harvesting. For this system, a PID controller based on BP neural network algorithm is designed and vehicle dynamic performances are studied. Based on the traditional energy-regenerative efficiency calculation, a novel self-supply energy efficiency concept is proposed to evaluate the utilization effect of the recycled energy for this dual-functional suspension. Simulations are carried out, and the results show that the vehicle dynamic performances are effectively improved under different input conditions, including road surfaces and vehicle speeds. Furthermore, the energy-regenerative suspension can recover part of vibration energy, where the self-supply energy efficiency is about 55% and the energy-regenerative efficiency is about 16%. Meanwhile, the BP-PID algorithm also enables the suspension system’s self-adaptability and stability characteristics on its energy recovery capability.

show abstract

“…e input of the l-th neuron in the output layer is net (3) l (k) � 5 i�1 w (3) li O (2) i + θ (3) l , l � 1, 2, 3,…”

Section: Bp-pid Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Energy‐Regenerative Active Suspension Based on BP Neural Network PID Control

Liu

Zhang

et al. 2019

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…The subject is still up to date, being undertaken by scientists conducting experimental and numerical research on suspensions of vehicles equipped with this type of dampers. [3][4][5] Recently, many other investigations (cf. previous studies [6][7][8][9][10] for example) provided additional know-how on developing effective vehicle, train, or aircraft active or semi-active suspension systems with the use of different smart materials.…”

Section: Introductionmentioning

confidence: 99%

Investigation of an off-road vehicle equipped with magnetorheological dampers

Makowski

Knap

2018

Advances in Mechanical Engineering

View full text Add to dashboard Cite

At present, owing predominantly to advances in measurement technology, microprocessor control and development of smart materials have witnessed a widespread adoption of semi-active damping systems based on magnetorheological fluid technology into everyday life. An important class of systems which utilize magnetorheological technology is constituted by vehicle vibration systems. These systems are generally equipped with different numbers of reciprocating or rotary dampers filled with magnetorheological fluid. Dampers and their properties are controlled by an electronic controller with embedded control algorithm. In this article, a simplified model of an off-road vehicle equipped with a realtime controlled semi-active suspension system with magnetorheological dampers is presented. In addition, a simple algorithm employed to control them is described. Algorithm efficacy has been verified by means of conducting numerous road and off-road experiments employing Honker 2000 vehicle. The main goal of this article is to present simplified yet efficient methods for controlling changes in properties of suspension of an off-road vehicle with magnetorheological dampers. These properties are controlled by an optimization algorithm responsible for an optimal selection of friction in magnetorheological dampers. The algorithm allows for a substantial improvement of the performance of an off-road vehicle suspension.

show abstract

“…15 This characteristic of LIPs is helpful to develop semi-active control algorithms. 19,20 The invariant points in the semi-active suspension which have similar properties to LIPs are named SIPs. It is of great potential in the semiactive suspension study.…”

Section: Introductionmentioning

confidence: 99%

Invariant points of semi-active suspensions

Nie

Zhuang

Chen

et al. 2018

Advances in Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

The invariant points of the quarter vehicle model shape many properties concerned in vehicle suspension design. Although they have been studied for years, the invariant points are still confusing for their different traits. Hence, the existing invariant points are sorted and analysed. Meanwhile, the invariant points of the semi-active suspension were introduced. In this article, a further study on the invariant points of the semi-active suspension is conducted, which provides insight for suspension optimization. In detail, an equivalent linear approximation model, derived from the transformation of the semi-active suspension model, is utilized to analyse the invariant points of the semi-active suspension. With the equivalent linear approximation model, the invariant points of the sky-hook semi-active suspension are proved to be highly dependent on the adjustable damping range. In fact, the frequencies and magnitudes of the invariant points, as the limit of the semi-active suspension, are determined by the adjustable damping range. Consequently, the influence rule of the adjustable damping range on semi-active suspension performance is revealed, which provides insight for the optimization of the damping for different demands. Experimental study shows that the invariant points are real, not just exist in the theoretical analysis.

show abstract

A semi-active suspension control algorithm for vehicle comprehensive vertical dynamics performance

Cited by 46 publications

References 24 publications

Modeling and Simulation of Energy‐Regenerative Active Suspension Based on BP Neural Network PID Control

Modeling and Simulation of Energy‐Regenerative Active Suspension Based on BP Neural Network PID Control

Investigation of an off-road vehicle equipped with magnetorheological dampers

Invariant points of semi-active suspensions

Contact Info

Product

Resources

About