Modelling and control of a Magneto-Rheological elastomer for impact reduction

Rahmat, Mohd Sabirin; Hudha, Khisbullah; Kadir, Zulkiffli Abd; Nuri, Nur Rashid Mat; Amer, Noor Hafizah; Abdullah, Shahrum

doi:10.15282/jmes.13.3.2019.04.0430

Cited by 9 publications

(7 citation statements)

References 25 publications

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“…Since the MRF damper was characterised for impact loading applications, an impact pendulum test rig with variations of pendulum masses was developed, as shown in Figure 2. The procedure to conduct the characterisation using the impact pendulum test rig is described in Rahmat et al (2019a) and(2019b). In this study, the measurement of transmitted force after the impact force dissipated by the MRF damper was calculated using data from a quasi-static test multiplied by the relative displacement of the movable plate during impact.…”

Section: Characterisation Of Mrf Dampermentioning

confidence: 99%

Modelling and validation of magneto-rheological fluid damper behaviour under impact loading using interpolated multiple adaptive neuro-fuzzy inference system

Rahmat

Hudha

Kadir

et al. 2020

MMMS

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PurposeThe objective of this paper is to develop a fast modelling technique for predicting magneto-rheological fluid damper behaviour under impact loading applications.Design/methodology/approachThe adaptive neuro-fuzzy inference system (ANFIS) technique was adopted to predict the behaviour of a magneto-rheological fluid (MRF) damper through experimental characterisation data. In this study, an MRF damper manufactured by Lord Corporation was used for characterisation using an impact pendulum test rig. The experimental characterisation was carried out with various impact energies and constant input currents applied to the MRF damper.FindingsThis research provided a fast modelling technique with relatively less error in predicting MRF damper behaviour for the development of control strategies. Accordingly, the ANFIS model was able to predict MRF damper behaviour under impact loading and showed better performance than the modified Bouc–Wen model.Research limitations/implicationsThis study only focused on modelling technique for a single type of MRF damper used for impact loading applications. It is possible for other applications, such as cyclic loading, random loadings and system identification, to be studied in future experiments.Original/ValueFuture researchers could apply the ANFIS model as an actuator model for the development of control strategies and analyse the control performance. The model also can be replicated in other industries with minor modifications to suit different needs.

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Section: Characterisation Of Mrf Dampermentioning

confidence: 99%

Modelling and validation of magneto-rheological fluid damper behaviour under impact loading using interpolated multiple adaptive neuro-fuzzy inference system

Rahmat

Hudha

Kadir

et al. 2020

MMMS

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“…Experimental measurements for recently produced refrigerators help to model the power consumption profile since this type of load has not yet been analyzed in details after employing variable speed controller. Several research related fields that discussed DC environment applications can also benefit from the outcomes of this study such as [22].…”

Section: Introductionmentioning

confidence: 97%

DC Environment for a Refrigerator With Variable Speed Compressor; Power Consumption Profile and Performance Comparison

Sabry

Ker

2020

IEEE Access

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DC power distribution in residential sector has regained interest among researchers and industrial players as new electronics-based appliances became locally available. However, the compatibility of appliances with DC distribution systems still requires much research effort. This work mainly explores on the power consumption profile of an inverter-driven Variable Speed Controller (VSC-based) refrigerator that has not yet been analyzed as one of the most important household loads. This paper compares the power consumption in two scenarios; 1) using three supply configurations for a VSC-based refrigerator, a Battery-Inverter-Load, a Battery-Load and Grid-Load, and 2) using a same AC power source to supply a VSC-based refrigerator and a same-size conventional refrigerator. This analysis helps toward modeling and energy estimation of PV system applications involving storage batteries. A wireless monitoring circuit has been employed to handle temperature, current and voltage measurements with a high sampling rate to cover the potential surge power. The experimental measurements show a better performance on using DC over AC power source and the power rate consumed has a smooth pattern at the starting-on time until approach a rated power. The measured efficiency of the Battery-Load topology approaches 99% compared to that of the Battery-inverter-load topology, which is approximately 78.5%. It is also found that the tested refrigerator with Battery-Load topology consumes an energy amounting to 1.850 kWh daily, while with Battery-inverterload topology consumes 2.466 kWh daily under the same operating conditions. These results can serve as a model for modeling refrigerators and other appliances that adopt speed controller technology to drive their motors.INDEX TERMS Compression refrigeration system, dc microgrid, total power consumption, building energy modeling, PV-battery systems.

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“…One approach is modeling the MRE with nonlinear terms to predict the complex property change by methods such as fuzzy inference [36] and a three-layer NARX neural network [37]. Another approach is designing a nonlinear model-based controller [14,38] such as a semi-active fuzzy control [39] and hybrid skyhook force control [40].…”

Section: Introductionmentioning

confidence: 99%

Design of model-free reinforcement learning control for tunable vibration absorber system based on magnetorheological elastomer

Park¹,

Lee²,

Kim³

2021

Smart Mater. Struct.

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This paper proposes a novel model-free reinforcement learning (RL) for vibration control of a magnetorheological elastomer (MRE)-based application. Because the modeling of the MRE stiffness is nonlinear and time-varying depending on various environmental factors, this paper approached the MRE control issue via a model-free learning based method. In this study, an RL model is designed for the MRE-based tunable vibration absorber (TVA) which can control the optimal stiffness of MRE to maximize the vibration suppression. The designed RL algorithm continuously learns and updates the optimal control input of the MRE stiffness adaptively to the dynamic environment without using any prior knowledge of the MRE modeling. From analyzing the mechanism of MRE TVA, the RL algorithm and parameters are carefully designed for a high vibration performance. Also, this study proposed several ideas to make the RL model simpler and converge at a high rate. The experiments confirmed that the proposed RL model showed a rapid convergence to the optimal policy which could minimize the vibration level with respect to the dynamic excitation disturbance. Results showed that the RL model had a similar performance as the conventional tuning method and suppressed the vibration level as much as 57% compared to the one without the controller. Also, the proposed RL algorithm was able to estimate the actual dynamics of the MRE TVA by learning from the environment. Thus, this study showed the feasibility of implementing a model-free RL model to realize an adaptive controller for applications based on highly nonlinear MRE.

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Modelling and control of a Magneto-Rheological elastomer for impact reduction

Cited by 9 publications

References 25 publications

Modelling and validation of magneto-rheological fluid damper behaviour under impact loading using interpolated multiple adaptive neuro-fuzzy inference system

Modelling and validation of magneto-rheological fluid damper behaviour under impact loading using interpolated multiple adaptive neuro-fuzzy inference system

DC Environment for a Refrigerator With Variable Speed Compressor; Power Consumption Profile and Performance Comparison

Design of model-free reinforcement learning control for tunable vibration absorber system based on magnetorheological elastomer

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