Fuzzy logic with a novel advanced firefly algorithm and sensitivity analysis for semi-active suspension system using magneto-rheological damper

Talib, Mat Hussin Ab; Darus, Intan Zaurah Mat; Samin, Pakharuddin Mohd

doi:10.1007/s12652-018-1044-4

Cited by 20 publications

(12 citation statements)

References 24 publications

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“…Substitute the variable (k + 1)T 2 t in (18) with the variable t, and the following formula can be obtained.…”

Section: Precise Discretization Of the Vehicle State Equationmentioning

confidence: 99%

“…In terms of control methods for MR damper, numerous control methods have been employed in the magneto-rheological semi-active suspension system, such as sky-hook control, 14 linear optimal control, 15 adaptive control, 16 sliding mode variable structure control, 17 fuzzy control, 18 neural network control, 19 and composite control including multiple control methods. 20,21 It should be noticed that the current research mainly places the emphasis on control methods of 1/4 magneto-rheological semi-active suspension system, but rarely involves vehicle attitude control on the basis of MR damper.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle attitude compensation control of magneto-rheological semi-active suspension based on state observer

Wang

Sheng

Ding

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper presents a vehicle attitude compensation algorithm based on state observer for vehicle semi-active suspension system equipped with four magneto-rheological dampers (MR dampers). The proposed algorithm including magneto-rheological damper control algorithm, attitude compensation control algorithm, and design method of state observer is to effectively improve ride comfort and control vehicle body attitude. First, the actual equivalent damping of magneto-rheological damper is introduced into state observer, and the parameter matrix of suspension system is updated in real time via precise discretization method to enhance the estimation accuracy of state observer. Then, the velocity signal estimated by state observer is employed as the evidence to realize attitude compensation control for vehicle body. Finally, relevant co-simulations and hardware-in-the-loop test are conducted to verify the validity of the proposed control algorithm. Results of simulations and tests demonstrate that the application of the control algorithm proposed in this paper can significantly improve ride comfort of magneto-rheological suspension and optimize vehicle body attitude.

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“…Substitute the variable (k + 1)T 2 t in (18) with the variable t, and the following formula can be obtained.…”

Section: Precise Discretization Of the Vehicle State Equationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle attitude compensation control of magneto-rheological semi-active suspension based on state observer

Wang

Sheng

Ding

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…Ajithapriyadarsini et al (2019) used differential evolution (DE) to optimize the gain of a fuzzy logic-DE algorithm-based PID controller. Ab Talib et al (2019) proposed an advanced firefly algorithm (AFA) for improving vehicle dynamics. Azizi et al (2019) used Multi-Verse Optimizer (MVO) for the optimization of a fuzzy controller applied to a seismically excited nonlinear building.…”

Section: The Optimization Of Fuzzy Inference Systemsmentioning

confidence: 99%

A Bee Colony Optimization (BCO) and Type-2 Fuzzy Approach to Measuring the Impact of Speed Perception on Motor Vehicle Crash Involvement

Čubranić-Dobrodolac

Švadlenka

Čičević

et al. 2021

Preprint

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This paper examines how a driver’s perception of various speed levels, as well as driver’s speed perception from different positions, affect the propensity for motor vehicle crashes (MVCs). Data collection is performed in twelve experiments. 178 young drivers assessed the speed level from four positions; three of them relate to the speed perception of other vehicles on the road, while the remaining one represents the assessment of own speed. At each position, three speed levels were assessed: 30, 50, and 70 km/h. To process data, seven Type-2 fuzzy inference systems (T2FISs) are designed and tested in a sense of compliance with the empirical data. As a result, a relationship between the various forms of speed perception and participation in MVCs can be quantified. To examine the initial conclusions, the optimization of each of these T2FISs is performed by implementing the bee colony optimization (BCO) metaheuristic. The BCO based algorithm proposed in this paper achieved an average improvement of 21.17% in the performance of the initial T2FIS structures. The final results indicate that the drivers whose speed perception of the vehicle they are looking at from the rear side, as well as of the own vehicle, is poor have an elevated risk toward participation in MVCs compared to other forms of speed perception. The best-found T2FIS structures can be used as a decision-making tool that quantifies the driver propensity for MVCs, which can be useful in various educational and recruitment procedures in the field of transportation and traffic safety.

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“…Modified Bouc-Wen model predicts the behavior of the damper very well in all regions, including in the region where the acceleration and velocity have opposite sign and the magnitude of the velocities are small [21,47]. The parameter estimation error for this model is 1.3770 for input current 0 to 1 Ampere that applied on the damper [25].…”

Section: Iiiiii Modified Bouc-wen Modelmentioning

confidence: 99%

A Comprehensive Review on the Related Models in Magneto-Rheological Automobile Suspension System

Yaakub¹,

Yahaya²,

Ahmad³

et al. 2020

International Journal of Engineering Research and Technology

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In this article, the Magneto-Rheological (MR) automobile suspension is thoroughly reviewed. MR suspension has a smart behaviour of the fluid and it is preferred in many industries. The paper reviews the different model which was developed to represent the behaviour of MR suspension in automobile systems. This paper also presents the advantages, drawback and parameter estimation error for all proposed models. The conclusion of the intelligent model used to represent the behaviour of the MR automobile suspension systems also described.

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Fuzzy logic with a novel advanced firefly algorithm and sensitivity analysis for semi-active suspension system using magneto-rheological damper

Cited by 20 publications

References 24 publications

Vehicle attitude compensation control of magneto-rheological semi-active suspension based on state observer

Vehicle attitude compensation control of magneto-rheological semi-active suspension based on state observer

A Bee Colony Optimization (BCO) and Type-2 Fuzzy Approach to Measuring the Impact of Speed Perception on Motor Vehicle Crash Involvement

A Comprehensive Review on the Related Models in Magneto-Rheological Automobile Suspension System

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