Journal ArticleDOI
Combination of particle swarm optimization algorithm and artificial neural network to propose an efficient controller for vehicle handling in uncertain road conditions
B. Aalizadeh,A. Asnafi +1 more
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TLDR
By combination of a simple PID and an optimized adaptive neural network controller, an arrangement for active front steering control of vehicles in different road frictions is proposed, which fulfills both the applicability and efficiency due to dual use of PID and neural network controllers.Abstract:
Due to fast variation of desired variables in vehicle handling problem, design of an accurate applicable economic and considerably quick responsible controller for steering control of such systems has attracted much attention in the literature. The problem becomes more complicated, if the variation of road condition comes into play also. In this study, by combination of a simple PID and an optimized adaptive neural network controller, an arrangement for active front steering control of vehicles in different road frictions is proposed. A general PID controller is picked up and then optimized using the particle swarm optimization algorithm. After that, a neural network is added consecutively and trained by the outputs of PID controller and neural network toolbox of MATLAB software. The proposed controller fulfills both the applicability and efficiency due to dual use of PID and neural network controllers. Simulation results confirm the rightness of suggested controller in active steering control of vehicles even for unpredictable road friction.read more
Citations
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Optimal choice of parameters for particle swarm optimization
TL;DR: The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature and guidelines for determining parameter values are given.
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Analyzing multimodal transportation problem and its application to artificial intelligence
TL;DR: To solve transportation problem by considering the multimodal transport systems and then utilize it to solve neural network (NN) problem in AI, a numerical example is included and a better impact for analyzing the real-life decision-making problems is revealed.
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Cognitive Competence Improvement for Autonomous Vehicles: A Lane Change Identification Model for Distant Preceding Vehicles
TL;DR: A comprehensive model consisting of a back-propagation (BP) neural network model optimized by a particle swarm optimization (PSO) algorithm, and a continuous identification model is developed based on the results of naturalistic on-road experiments using millimeter-wave radar data, which meets the recognition requirements of the autonomous driving systems for distant targets.
Journal ArticleDOI
The Application of Particle Swarm Optimization in Neural Networks
Lizhen Gao,Yan-yan Lin +1 more
TL;DR: This dissertation studies the neural network model based on the prerequisite that the branch network research management system retains the advantages of the traditional model and tracks the optimal value of current search through cross and mutation.
Journal ArticleDOI
Comparison of neural network and neurofuzzy identification of vehicle handling under uncertainties
TL;DR: 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 neurofBuzzy model is more precise and accurate than neural network in the training condition.
References
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Proceedings ArticleDOI
Particle swarm optimization
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Proceedings ArticleDOI
A discrete binary version of the particle swarm algorithm
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