Active following fuzzy output feedback sliding mode control of real-vehicle semi-active suspensions

Liu, H.; Nonami, Kenzo; Hagiwara, Toru

doi:10.1016/j.jsv.2008.01.032

Cited by 28 publications

(15 citation statements)

References 14 publications

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“…These algorithms generally operate as predictive models, where the main objective is to determine the actions to be executed by energy-dissipating devices to reduce the system's response to dynamic actions. Additionally controllers employing fuzzy logic act by continually adjusting the dampers' control forces using a set of rules representing the heuristic knowledge of the particular system [9,[21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of semi-active control algorithms applied to magnetorheological dampers

Lara-V.

Brito²,

2017

Ingeniare. Rev. chil. ing.

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This paper analyzes the performance of three different semi-active control algorithms used to calculate and manage the optimal damping forces generated by a pair of magnetorheological (MR) dampers installed in a two-story building. The semi-active algorithms used are the linear quadratic regulator (LQR) associated with the clipped optimal algorithm, an algorithm based on a prediction model and a dynamic inverse model using nonlinear autoregressive exogenous (NARX)-type artificial neural networks, and a decision-making algorithm based on fuzzy logic. Additionally, the performances of the proposed semi-active algorithms are compared to those of passively used control devices. The results show response value reductions exceeding 50% for all the semi-active control strategies, which considerably outperformed the passive control. The three control strategies are thus confirmed as interesting tools with potential applications as MR damper administrators.Keywords: Structure dynamics, semi-active control of structures, vibration reduction, magnetorheological dampers, control algorithms, intelligent systems. RESUMEN En este artículo se analiza numéricamente la eficiencia y el desempeño de tres diferentes algoritmos de control semiactivo que tienen la función de calcular y administrar las fuerzas óptimas de amortiguamiento que deben ser generadas por un par de amortiguadores magnetoreológicos instalados en una estructura de dos niveles. Los algoritmos semi-activos empleados son el regulador lineal cuadrático (LQR), asociado al algoritmo clipped optimal y dos algoritmos fundamentados en sistemas inteligentes: un algoritmo basado en un modelo de predicción y en un modelo inverso dinámico construidos por medio de redes neuronales artificiales de tipo NARX y un algoritmo de toma de decisiones basados en lógica difusa. Adicionalmente, la actuación de los algoritmos de control semi-activo propuestos es comparada con el desempeño de los

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Section: Introductionmentioning

confidence: 99%

Comparative analysis of semi-active control algorithms applied to magnetorheological dampers

Lara-V.

Brito²,

2017

Ingeniare. Rev. chil. ing.

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“…Many FSMC schemes have been proposed and the chattering phenomena in the controlled system can be avoided by using the fuzzy sliding surface in the reaching condition of the SMC. Liu et al 7 presented a new active following fuzzy output feedback sliding-mode controller for a real-vehicle semi-active suspension system by using four stepping motors. In the experiment, it was shown that the proposed method is effective in stabilizing the heave, roll and pitch movement of the car body.…”

Section: Introductionmentioning

confidence: 99%

Effect of a hybrid controller on ride comfort under random road excitation for a semi-active suspension system

Wang

Shih

2012

Proceedings of the Institution of Mechanical Engineers, Part D:

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An adjustable damper is able to achieve effective anti-vibration performance by changing the damping coefficient. Therefore, this study experimentally evaluated the dynamic responses of the adjustable damper, which was subsequently incorporated into a quarter-car system. The current research employed a hybrid controller with a self-tuning fuzzy sliding-mode controller and a self-organizing learning mechanism under random road excitation (ISO 8608:1995) to verify the reliability. A single-input single-output fuzzy inference was used to adjust the slope of the sliding surface. Furthermore, the self-organizing control was developed to self-organize online fuzzy sliding rules. This study also investigated the ride comfort of the car body according to the ISO 2631-1:1997 standard. The experimental results show that the proposed controller demonstrates notable improvements in the vibration suppression of the car body and in the ride comfort, which is superior to the compared mixed skyhook-acceleration-driven damping controller.

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“…In simpler cases the PID control can be applied. Active control systems may also use regulators based on neural networks [11] and fuzzy logics [12]. The control schemes that are commonly used for semiactive suspension systems include the 'sky-hook' control strategy [13] where the damping force is related to the absolute velocity of the vehicle body.…”

Section: Introductionmentioning

confidence: 99%

The Active Suspension of a Cab in a Heavy Machine

Tora¹

2012

Advances on Analysis and Control of Vibrations - Theory and Applications

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Active following fuzzy output feedback sliding mode control of real-vehicle semi-active suspensions

Cited by 28 publications

References 14 publications

Comparative analysis of semi-active control algorithms applied to magnetorheological dampers

Comparative analysis of semi-active control algorithms applied to magnetorheological dampers

Effect of a hybrid controller on ride comfort under random road excitation for a semi-active suspension system

The Active Suspension of a Cab in a Heavy Machine

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