Combination of particle swarm optimization algorithm and artificial neural network to propose an efficient controller for vehicle handling in uncertain road conditions

Aalizadeh, B.; Asnafi, Alireza

doi:10.1007/s00521-016-2689-6

Cited by 11 publications

(10 citation statements)

References 28 publications

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“…McCulloch and Pitts (1943) introduced Neural networks in the 1940s by presenting the first mathematical model of biological neurons. Artificial intelligence and neural networks have been discussed and proven as an efficient tool in the fields relevant to vehicle handling and transportation (see, for example, Aalizadeh and Asnafi (2018); Lin et al (2017); Huang et al (2017) and Wang and Chen (2018)).…”

Section: Identification Modelmentioning

confidence: 99%

Comparison of neural network and neurofuzzy identification of vehicle handling under uncertainties

Aalizadeh

2019

Transactions of the Institute of Measurement and Control

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In this study, two adaptive neural network and neurofuzzy identification models are proposed to identify vehicle handling under uncertainties. These models are used to identify vehicle handling in different road friction coefficients and velocities. These two identification models modify their weights to cope with uncertainties using back propagation of error as a learning algorithm. However, an adaptive model has some limitations to identify real systems. The ability of adaptation is not the same for all identification models; some models are more robust to cope with a specific uncertainty or a wider range of uncertainties. In this study, adaptiveness of two identification models are compared under two different uncertainties. First, a precise model in CARSIM software is simulated and a set of input/output data of vehicle response are collected. Then an initial three-layer neural network is trained in MATLAB software. In addition, a Neurofuzzy model is also trained in ANFIS (adaptive neurofuzzy inference system) toolbox of MATLAB software. Then this trained model is applied to the vehicle in different maneuvers, velocities and road friction coefficients. Results show that proposed neural network identifies the vehicle handling more efficiently than neurofuzzy model in conditions that are away from training condition. However, proposed neurofuzzy model is more precise and accurate than neural network in the training condition.

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Section: Identification Modelmentioning

confidence: 99%

Comparison of neural network and neurofuzzy identification of vehicle handling under uncertainties

Aalizadeh

2019

Transactions of the Institute of Measurement and Control

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“…Here, a fuzzy logic system which takes advantage of the neural network to adapt its parameter according to unpredictable changes is employed (see following sections for more details). In this study, the backpropagation of error [11,12], as a training algorithm, is selected. More specifically, the input and output data of the system under study are collected for three well-known manoeuvres i.e.…”

Section: Identification Of Modelmentioning

confidence: 99%

“…After that, the network is expected to find the appropriate response for a new input relative to its information background; exactly as it does in the process of experiential learning. The efficiency and accuracy of neural networks has been discussed in several studies in the literature (see for example [11,12]). Usually, the mechanism for finding the best answer is performed by minimising a cost function that correlates the identified model response and the actual system.…”

Section: Training Algorithm For the Neuro-fuzzy Systemmentioning

confidence: 99%

“…6) must be in the opposite direction of the gradient of the error E i.e. (11) Finally, the following relation for the weight modification procedure can be obtained:…”

Section: Training Algorithm For the Neuro-fuzzy Systemmentioning

confidence: 99%

“…Zhang and Wang [10] proposed a controller-gain tuning approach integrating active front steering system and direct yaw moment to enhance vehicle handling under velocity change uncertainties while the road coefficient of friction was not considered as a source of uncertainty. Aalizadeh and Asnafi [11,12] studied the stability and handling of a specific vehicle in uncertain road condition using a combination of artificial neural networks and bio-inspired optimisation algorithms. Aalizadeh [13] investigated the effects of uncertain road conditions on vehicle handling and proposed a neuro-fuzzy controller to enhance system performance.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Versus Neuro-fuzzy Identification of Active Front Steering of a Vehicle on Slippery Roads

Aalizadeh

Asnafi

2019

IJAME

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Due to the high rate changes in the handling of cars, the use of an auxiliary identification process to design efficient controllers is of importance. Many identification algorithms have been proposed in the literature, which generally performs well under normal situations, but does not show acceptable performance in uncertain conditions. In this article, due to the nature of the neuro-fuzzy networks in identifying and predicting uncertain conditions, an adaptive neuro-fuzzy identification algorithm is proposed to steer vehicles at the uncertain slippery condition of roads. A set of data for three well-known manoeuvres of vehicle dynamics at conventional conditions was collected to train the algorithm using adaptive neuro-fuzzy inference system of MATLAB. Using back propagation of error as the learning algorithm, the parameters of the algorithm were modified regarding uncertain conditions. Making an analogy, the performance of the proposed identification scheme was compared to the untrained fuzzy one. In regular situations, the results were almost identical, but in uncertain ones such as slippery roads, the performance of the proposed neuro-fuzzy algorithm was much better.

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Sensorineural Hearing Loss Identification via Discrete Wavelet Packet Entropy and Cat Swarm Optimization

Wang

Yang

Liu

et al. 2019

Applied Nature-Inspired Computing: Algorithms and Case Studies

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Combination of particle swarm optimization algorithm and artificial neural network to propose an efficient controller for vehicle handling in uncertain road conditions

Cited by 11 publications

References 28 publications

Comparison of neural network and neurofuzzy identification of vehicle handling under uncertainties

Comparison of neural network and neurofuzzy identification of vehicle handling under uncertainties

Fuzzy Versus Neuro-fuzzy Identification of Active Front Steering of a Vehicle on Slippery Roads

Sensorineural Hearing Loss Identification via Discrete Wavelet Packet Entropy and Cat Swarm Optimization

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