A Novel Sliding Mode Control Algorithm for an Active Suspension System Considering with the Hydraulic Actuator

Nguyen, Duc Ngoc; Nguyen, Tuan Anh; Dang, Ngoc Duyen

doi:10.1590/1679-78256883

Cited by 16 publications

(12 citation statements)

References 48 publications

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“…The logic modes of this method can respond well to external stimuli [47,48]. Besides, many other intelligent control methods have also been applied to the active suspension system [49][50][51][52][53].…”

Section: Literature Reviewmentioning

confidence: 99%

LQR Control with the New Triple In-Loops Algorithm for Optimization of the Tuning Parameters

Nguyen¹,

Nguyen²,

Dang³

et al. 2022

MMEP

Self Cite

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The suspension system plays a role in ensuring the stability of the vehicle when traveling on the road. On many modern vehicles, the active suspension system has been proposed to replace the conventional passive suspension system. The performance of the controller for the active suspension system depends on its control method. In this paper, a half dynamics model of the vehicle is established. Besides, the LQR control method is also used. The parameters of the control matrix are calculated through the triple in-loop optimization algorithm, which has been shown in the research. This is a completely novel algorithm. This algorithm helps to choose the most optimal parameters. Thus, it ensures the efficiency and stability of the controller. The calculation and comparison process are done automatically. When the loop ends, the optimal parameters are explicitly indicated. The simulation process is done in the MATLAB-Simulink environment. The results of the research showed that when the LQR controller, which was optimized through the triple in-loop algorithm used, the vehicle's oscillation was significantly reduced. In the three survey situations, the values of the roll angle and the angular acceleration of the sprung mass are guaranteed to be stable. Besides, when using this controller, the phenomenon of “chattering” after the excitation ends does not appear. This topic can be further developed in the future.

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Section: Literature Reviewmentioning

confidence: 99%

LQR Control with the New Triple In-Loops Algorithm for Optimization of the Tuning Parameters

Nguyen¹,

Nguyen²,

Dang³

et al. 2022

MMEP

Self Cite

View full text Add to dashboard Cite

show abstract

“…It guarantees the stability of the system as per changing conditions. 148,149 In addition, the performance analysis of various motors with numerous control strategies for brake energy recovery in EVs, as mentioned in Table 7. Many researchers combine two or more control strategies to obtain the best results of both.…”

Section: Comparative Assessment Of Various Braking Control Strategies...mentioning

confidence: 99%

“…These learning‐based approaches in RBS can be beneficial in a variety of ways because they are closely related to human learning behaviour and tend to improve over time. It guarantees the stability of the system as per changing conditions 148,149 . In addition, the performance analysis of various motors with numerous control strategies for brake energy recovery in EVs, as mentioned in Table 7.…”

Section: Regenerative Braking Control Strategies In Electric Vehiclesmentioning

confidence: 99%

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Saiteja

Ashok

Wagh

et al. 2022

Intl J of Energy Research

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Summary Vehicle technology advances and the world shifts towards E‐mobility, improving the performance of electric vehicles (EVs) and HEVs, which are becoming the prominent focus. One of the prime topics of EV development is increasing the driving range, which is the fundamental requirement. Apart from increasing the battery capacity, retrieving the wasted energy during conventional braking, also called as regenerative braking, is a hot topic. In this context, numerous control architectures and major braking approaches are considered to examine in this study in order to create an efficient regenerative braking system (RBS). This review article intends to provide an overview of major subsystems in the RBS such as motor control system and hydraulic braking system and how it affects the braking performance is also well discussed. Additionally, it complies with some of the recent research applications and systematically reviews the several braking control strategies implied. The prominent ones are fuzzy logic control, neural network, MPC, sliding mode, and adaptive control modelling approaches. These control strategies are used to enhance energy regeneration without affecting vehicle performance. Further, this article discusses the RBS design process and its calibration variables, such as speed of the vehicle and brake force estimation, which can be used to improve braking performance. Moreover, challenges on RBS improvements are effectively addressed, coupled with brief suggestions and discussions for the growth of future RBS development. Finally, this article will hopefully help the reader to critically analyse the working of RBS and encourage to design of an efficient RBS for electro‐mobility application. Highlights A holistic overview of the RBS control system architecture and its various control systems is reviewed. Various brake energy control strategies like Fuzzy, MPC, NN, SMC, Adaptive and learning‐based controls are critically evaluated. The discussion of necessary calibration process and its associated parameters are elucidated. Representation of real‐time design and development process of an efficient RBS in EV. Some of the prominent challenges faced during design and development of RBS and scope of future improvement is suggested.

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“…For this algorithm, it is necessary to determine the state variables of the oscillatory state matrix 26 . If hydraulic actuators are considered, the number of possible state variables is 5 (for a quarter-dynamics model), or 10 (for a half-dynamics model) 27,28 . This will make the process of derivation of state variables extremely difficult.…”

Section: List Of Symbolsmentioning

confidence: 99%

Evaluate the stability of the vehicle when using the active suspension system with a hydraulic actuator controlled by the OSMC algorithm

Nguyen

2022

Sci Rep

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The ride comfort is controlled by the suspension system. In this article, an active suspension system is used to control vehicle vibration. Vehicle oscillations are simulated by a quarter-dynamic model with five state variables. This model includes the influence of the hydraulic actuator in the form of linear differential equations. This is a completely novel model. Besides, the OSMC algorithm is proposed to control the operation of the active suspension system. The controller parameters are optimized by the in-loop algorithm. According to the results of the study, under normal oscillation situations, the maximum and average values of the sprung mass were significantly reduced when the OSMC algorithm was applied. In dangerous situations, the wheel is completely separated from the road surface if the vehicle uses only the passive suspension system or active suspension system with a conventional linear control algorithm. In contrast, the interaction between the wheel and the road surface is always guaranteed when the OSMC algorithm is used to control the operation of the active suspension system. The efficiency that this algorithm brings is very high.

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A Novel Sliding Mode Control Algorithm for an Active Suspension System Considering with the Hydraulic Actuator

Cited by 16 publications

References 48 publications

LQR Control with the New Triple In-Loops Algorithm for Optimization of the Tuning Parameters

LQR Control with the New Triple In-Loops Algorithm for Optimization of the Tuning Parameters

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Evaluate the stability of the vehicle when using the active suspension system with a hydraulic actuator controlled by the OSMC algorithm

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