Neural network-based fuzzy inference system for speed control of heavy duty vehicles with electronic throttle control system

Eski, İkbal; Yıldırım, Şahin

doi:10.1007/s00521-016-2362-0

Cited by 26 publications

(15 citation statements)

References 21 publications

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“…The ANFIS model is also applicable in the case of hybrid drives where it minimizes engine fuel consumption with internal combustion and maximizes torque (Mohebbi et al, 2005). Eski & Yıldırım (2017) describe the use of ANFIS model for the electronic regulation of throttle of heavy vehicles. Car parking is a demanding action, sometimes for experienced drivers, and if it is a truck with a trailer, the problem becomes very complex.…”

Section: Vehicle Steering and Controlmentioning

confidence: 99%

Application of the ANFIS model in road traffic and transportation: A literature review from 1993 to 2018

Stojčić

2019

Oper. Res. Eng. Sci. Theor. Appl.

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The paper's focus is on researching the application of the ANFIS (Adaptive Neuro Fuzzy Inference System) model in traffic and transport through a review of relevant papers. The ANFIS, as an element of artificial intelligence, is widely used in intelligent transport systems. All collected papers are divided into 7 sub-areas, namely: 1) vehicle routing, 2) traffic control at intersections with light signaling, 3) vehicle steering and control, 4) safety, 5) modeling of fuel consumption, engine performance and exhaust emissions, 6) traffic congestion prediction, and 7) other applications. For each sub-area, the analysis of the proposed models is performed with a tabular overview of respective input and output variables, while in the third section the discussion of the results is given. It is found that the steering and control of vehicles represent a sub-area with the highest percentage in the total number of examined papers, while the security applications take second place.

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Section: Vehicle Steering and Controlmentioning

confidence: 99%

Application of the ANFIS model in road traffic and transportation: A literature review from 1993 to 2018

Stojčić

2019

Oper. Res. Eng. Sci. Theor. Appl.

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“…[18] firstly introduced the fuzzy neural network to predict delayed time for process control. Then as presented in [19], an adaptive neuro-fuzzy inference system (ANFIS), which integrates neural network with fuzzy inference system, was deployed to control the speed of a heavy duty vehicle. Not only for control system, but also for mobile learning system, [20,21] incorporated ANFIS as a reasoning engine to deliver learning content for mobile learning system.…”

Section: Fuzzy Logic and Evolving Fuzzy Neural Network (Efunn)mentioning

confidence: 99%

Effective long-term travel time prediction with fuzzy rules for tollway

Li¹,

Rose

Chen

et al. 2017

Neural Comput & Applic

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Abstract:Advanced traveller information system is an important Intelligent Transportation Systems (ITS) application area, which provides information to transport users and managers in order to improve the efficiency and effectiveness of the transportation system, in the face of increasing congestion in urban cities around the world. So far very limited research attention has been focused on long term travel time prediction (i.e. predicting greater than 60 minutes ahead). Long term travel time forecasts can play a critical role in journey planning decisions for both private road users and logistics operators. In this paper, we have considered a fuzzy neural network incorporated with both imprecise and numerical information and developed a hybrid long term travel time prediction model, which shows the better prediction capability than naive methods and highlights the importance of different data variables. The model combines the learning ability of neural networks and the knowledge extraction ability of fuzzy inference systems. The model was validated by using travel time data compiled from electronic toll tags on a 14 km length section of the CityLink tollway in Melbourne, Australia. The validation results highlight the ability of the fuzzy neural network model to accommodate imprecise and linguistic input information, while providing reliable predictions of travel times up to a few days ahead.

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“…Eski and Yildirim (2017) made a performance comparison among four controllers such as PID controller, model-based neural network controller, adaptive neural network-based fuzzy inference controller and robust adaptive neural-based fuzzy inference controller in terms of transient characteristics of position-controlled electronic throttle valve system. The simulated results showed that robust adaptive neural-based fuzzy inference controller outperforms the other suggested controllers [20]. Wang et al (2016) proposed a robust adaptive scheme for position control of electronic throttle (ET) valve.…”

Section: Introductionmentioning

confidence: 99%

Design and Comparative Study of Advanced Adaptive Control Schemes for Position Control of Electronic Throttle Valve

Humaidi

Hameed

2019

Information

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This paper investigates the performance of two different adaptive control schemes for controlling the angular position of an electronic throttle (ET) plate. The adaptive backstepping controller and adaptive sliding mode backstepping controller are the controllers under consideration. The control design based on these adaptive controllers is firstly addressed and the stability analysis of each controller has been presented and the convergence of both position and estimation errors for both controllers have been proved. A comparison study of the performance of both controllers has been conducted in terms of system transient characteristics and the behavior of their associated adaptive gain. The simulation has been implemented within the environment of the MATLAB package.

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Neural network-based fuzzy inference system for speed control of heavy duty vehicles with electronic throttle control system

Cited by 26 publications

References 21 publications

Application of the ANFIS model in road traffic and transportation: A literature review from 1993 to 2018

Application of the ANFIS model in road traffic and transportation: A literature review from 1993 to 2018

Effective long-term travel time prediction with fuzzy rules for tollway

Design and Comparative Study of Advanced Adaptive Control Schemes for Position Control of Electronic Throttle Valve

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