Data-Based Modeling of Vehicle Crash Using Adaptive Neural-Fuzzy Inference System

Zhao, Lin; Pawlus, Witold; Karimi, Hamid Reza; Robbersmyr, Kjell G.

doi:10.1109/tmech.2013.2255422

Cited by 30 publications

(10 citation statements)

References 40 publications

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“…In order to overcome the friction and disturbances of the road, which are the main sources of nonlinearity in the electric power steering systems, Takagi-Sugeno fuzzy model is used to represent the non-linearity of the system, and stabilization conditions are established based on linear matrix inequality. In Zhao, Pawlus, Karimi, and Robbersmyr (2014), an adaptive neural-fuzzy inference systems are used in data-based modeling of vehicle crash. A robust observer for unknown input Takagi-Sugeno models is designed in Chadli and Karimi (2013).…”

Section: Introductionmentioning

confidence: 99%

Interval Type-2 fuzzy logic controller design for the speed control of DC motors

Hassani

Zarei²

2015

Systems Science & Control Engineering

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In this paper, an optimal interval Type-2 Fuzzy controller is designed for the speed control of DC motors. In this way, first, the importance and position of Type-2 fuzzy systems are mentioned. In addition, some properties of Type-2 operators are investigated as well as the properties of membership degree of Type-2 fuzzy sets. A comparison between different parts of Type-1 and Type-2 fuzzy systems, such as fuzzifier, fuzzy inference engine, rule-base and defuzzifier is given. Finally, an Interval type-2 Fuzzy logic controller is implemented for the speed control of DC motor for the cases of series and shunt. The motor is considered under both the load disturbances and disturbance free conditions. The obtained results for different conditions are compared in tables and figures. The results show that in the disturbance free case, both controllers have acceptable performance, however, when the system is affected by disturbance interval Type-2 controller has better performance.

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Section: Introductionmentioning

confidence: 99%

Interval Type-2 fuzzy logic controller design for the speed control of DC motors

Hassani

Zarei²

2015

Systems Science & Control Engineering

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“…Moradi et al [27], proposed a FEM that can be utilized in the design process of a vehicle by reducing the aggressivity of the vehicle and increasing the on-road fleet compatibility in order to minimize the occupant injury. In [28], the authors developed a numerical model of a car crash by analysing the scenarios where a high-speed vehicle was crashing into a wall and a static vehicle.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Vehicle Crashworthiness Parameters Using Piecewise Lumped Parameters and Finite Element Models

Munyazikwiye¹,

Vysochinskiy²,

Khadyko³

et al. 2018

Preprint

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Estimating the vehicle crashworthiness parameters experimentally is expensive and time consuming. For these reasons different modelling approaches are utilized to predict the vehicle behaviour and reduce the need for full-scale crash testing. The earlier numerical methods used for vehicle crashworthiness analysis were based on the use of lumped parameters models (LPM), a combination of masses and nonlinear springs interconnected in various configurations. Nowadays, the explicit nonlinear finite element analysis (FEA) is probably the most widely recognized modelling technique. Although informative, finite element models (FEM) of vehicle crash are expensive both in terms of man-hours put into assembling the model and related computational costs. A simpler analytical tool for early analysis of vehicle crashworthiness could greatly assist the modelling and save time. In this paper a simple piecewise LPM composed of a mass-spring-damper system, is used to estimate the vehicle crashworthiness parameters, focusing on the dynamic crush and the acceleration severity index (ASI). The model is first calibrated against a full-scale crash test and a FEM, post-processed with the LS-DYNA software, at an impact velocity of 56 km/h. The genetic algorithm is used to calibrate the model by estimating the piecewise lumped parameters (stiffness and damping of the front structure of the vehicle). After calibration, the LPM is applied to a range of velocities (40, 48, 64 and 72 km/h). The predictions for crashworthiness parameters from the LPM were compared with the predictions from the FEA and the results are much similar. It is shown that the LPM can assist in crash analysis, since LPM has some predictive capabilities and requires less computation time in comparison with the explicit nonlinear FEA.

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“…[21], the authors discussed the real-time implementation of ANFIS control and the studied plant was a renewable interfacing inverter in a 3P4W distribution network environment. Other applications can be seen in [22][23][24][25] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Cycle-based ammonia-coverage-ratio reference generator design for Diesel engine two-cell selective catalytic reduction systems via a fuzzy approach

Zhang

Wang

2015

Fuel

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Data-Based Modeling of Vehicle Crash Using Adaptive Neural-Fuzzy Inference System

Cited by 30 publications

References 40 publications

Interval Type-2 fuzzy logic controller design for the speed control of DC motors

Interval Type-2 fuzzy logic controller design for the speed control of DC motors

Prediction of Vehicle Crashworthiness Parameters Using Piecewise Lumped Parameters and Finite Element Models

Cycle-based ammonia-coverage-ratio reference generator design for Diesel engine two-cell selective catalytic reduction systems via a fuzzy approach

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