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Hamid Taghavifar
Hamid Taghavifar
Assistant Professor, Concordia University
Подтвержден адрес электронной почты в домене concordia.ca - Главная страница
Название
Процитировано
Процитировано
Год
MME-EKF-based path-tracking control of autonomous vehicles considering input saturation
C Hu, Z Wang, H Taghavifar, J Na, Y Qin, J Guo, C Wei
IEEE Transactions on Vehicular Technology 68 (6), 5246-5259, 2019
1802019
Investigating the effect of velocity, inflation pressure, and vertical load on rolling resistance of a radial ply tire
H Taghavifar, A Mardani
Journal of Terramechanics 50 (2), 99-106, 2013
1762013
Path-tracking of autonomous vehicles using a novel adaptive robust exponential-like-sliding-mode fuzzy type-2 neural network controller
H Taghavifar, S Rakheja
Mechanical Systems and Signal Processing 130, 41-55, 2019
1392019
A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility
H Taghavifar, A Mardani, L Taghavifar
Measurement 46 (8), 2288-2299, 2013
1152013
Prognostication of energy consumption and greenhouse gas (GHG) emissions analysis of apple production in West Azarbayjan of Iran using Artificial Neural Network
H Taghavifar, A Mardani
Journal of Cleaner Production 87, 159-167, 2015
1132015
A novel approach to energy harvesting from vehicle suspension system: Half-vehicle model
C Wei, H Taghavifar
Energy 134, 279-288, 2017
1062017
RISE-based integrated motion control of autonomous ground vehicles with asymptotic prescribed performance
C Hu, H Gao, J Guo, H Taghavifar, Y Qin, J Na, C Wei
ieee transactions on systems, man, and cybernetics: systems 51 (9), 5336-5348, 2019
912019
Off-road vehicle dynamics
H Taghavifar, A Mardani
Studies in Systems, Decision and Control 70, 37, 2017
902017
Effect of velocity, wheel load and multipass on soil compaction
H Taghavifar, A Mardani
Journal of the Saudi Society of Agricultural Sciences 13 (1), 57-66, 2014
822014
Appraisal of artificial neural networks to the emission analysis and prediction of CO2, soot, and NOx of n-heptane fueled engine
H Taghavifar, H Taghavifar, A Mardani, A Mohebbi, S Khalilarya, ...
Journal of Cleaner Production 112, 1729-1739, 2016
792016
Risk-based autonomous vehicle motion control with considering human driver’s behaviour
C Wei, R Romano, N Merat, Y Wang, C Hu, H Taghavifar, ...
Transportation research part C: emerging technologies 107, 1-14, 2019
762019
A non‐linear fractional‐order type‐3 fuzzy control for enhanced path‐tracking performance of autonomous cars
A Mohammadzadeh, H Taghavifar, C Zhang, KA Alattas, J Liu, MT Vu
IET Control Theory & Applications 18 (1), 40-54, 2024
752024
Application of artificial neural networks for the prediction of traction performance parameters
H Taghavifar, A Mardani
Journal of the Saudi Society of Agricultural Sciences 13 (1), 35-43, 2014
742014
Adaptive robust nonlinear active suspension control using an observer-based modified sliding mode interval type-2 fuzzy neural network
H Taghavifar, A Mardani, C Hu, Y Qin
IEEE Transactions on Intelligent Vehicles 5 (1), 53-62, 2019
722019
In-wheel motor vibration control for distributed-driven electric vehicles: A review
Z Zhao, H Taghavifar, H Du, Y Qin, M Dong, L Gu
IEEE Transactions on Transportation Electrification 7 (4), 2864-2880, 2021
682021
Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices
H Taghavifar, A Mardani
Energy 68, 651-657, 2014
682014
Neural network autoregressive with exogenous input assisted multi-constraint nonlinear predictive control of autonomous vehicles
H Taghavifar
IEEE Transactions on Vehicular Technology 68 (7), 6293-6304, 2019
642019
Energy consumption analysis of wheat production in West Azarbayjan utilizing life cycle assessment (LCA)
H Taghavifar, A Mardani
Renewable Energy 74, 208-213, 2015
562015
A novel terramechanics-based path-tracking control of terrain-based wheeled robot vehicle with matched-mismatched uncertainties
H Taghavifar, S Rakheja
IEEE Transactions on Vehicular Technology 69 (1), 67-77, 2019
522019
A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles
H Taghavifar, A Mardani
Energy 66, 569-576, 2014
522014
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