<i>H∞</i>optimal control of vehicle active suspension systems in two time scales

Zhi-jun, FU; Dong, Xiaoyang

doi:10.1080/00051144.2021.1935610

Cited by 14 publications

(8 citation statements)

References 29 publications

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“…For this method, the equations describing the vehicle's oscillation are rewritten in the form of state matrices [34]. At the same time, the control signal is stable against the change of external factors [35][36][37][38][39]. Even the active suspension system controlled by the Robust controller has been used on many current electric vehicle models [40].…”

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

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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

View full text Add to dashboard Cite

show abstract

“…For simplicity, several weighting functions are removed. While satisfying the condition specified by (12), there exist three algorithms for solving the optimization problem defined in (10). The solutions are three designs: (1) an H ∞ suboptimal controller, (2) an H ∞ LSC controller, and (3) an µ analysis and synthesis controller [16].…”

Section: 3𝐻 Loop-shaping Controllermentioning

confidence: 99%

“…In these LQG/LQR-based AVS designs, forward speed, payload, center of gravity (CG) position, and other system parameters are generally assumed as constants. It is demonstrated that these LQG/LQR-based controllers exhibit poor robustness to operating condition variations, parametric uncertainties, unmodelled dynamics, and external disturbances [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Design Synthesis Method for Robust Controllers of Active Vehicle Suspensions

Zhu

2022

Designs

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This paper presents a design synthesis method for robust controllers of active vehicle suspensions (AVSs). Various control techniques have been applied to the design of AVSs for enhancing ride comfort and handling performance of ground vehicles. However, most of these model-based controller designs show poor robustness when the vehicle models are not accurate and operating conditions vary. To address the poor robustness problem of AVSs, a new controller is designed using the H∞ loop-shaping control technique. The controller targets robustness issues on vehicle models with parametric uncertainties and unmodelled dynamics. To facilitate the robust controller design, a design synthesis method is proposed: the H∞ loop-shaping controller design is formulated as a multi-objective optimization problem, the weighting functions’ parameters of the controller are treated as design variables, the expensive computing loads are handled by a parallel computing technique, and the solution of the optimization problem is the desired robust AVS controller. Simulation results demonstrate the benefits of the proposed AVS design.

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“…The suspension system is located between the axle and the body of the vehicle. It divides the vehicle into two separate parts, including the sprung mass and the unsprung mass 2 . When the vehicle’s body vibrates, the internal forces generated by the suspension system act on both parts of the mass to control the vibrations.…”

Section: Introductionmentioning

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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H∞optimal control of vehicle active suspension systems in two time scales

Cited by 14 publications

References 29 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

A Design Synthesis Method for Robust Controllers of Active Vehicle Suspensions

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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