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Anticipation-Based Autonomous Platoon Control Strategy with Minimum Parameter Learning Adaptive Radial Basis Function Neural Network Sliding Mode Control
Journal Article
10-06-03-0017
ISSN: 2380-2162, e-ISSN: 2380-2170
Sector:
Topic:
Citation:
Negash, N. and Yang, J., "Anticipation-Based Autonomous Platoon Control Strategy with Minimum Parameter Learning Adaptive Radial Basis Function Neural Network Sliding Mode Control," SAE Int. J. Veh. Dyn., Stab., and NVH 6(3):247-265, 2022, https://doi.org/10.4271/10-06-03-0017.
Language:
English
Abstract:
This article investigates the headway and optimal velocity tracking of autonomous
vehicles (AVs), considering their predictive driving for the stability and
integrity of spatial vehicle formation in the platoon. First, the human-like
anticipation car-following model is used for modeling the autonomous system.
Second, an adaptive radial basis function neural network (ARBF-NN)-based sliding
mode control (SMC) is proposed for the control purpose. The control objective is
to regulate traffic perturbation during entire road operations. To enable the
controller to experience less computational burden and adaptation complexity, a
minimum parameter learning (MPL) has also been integrated with ARBF-NN-based
SMC. Third, an illustrative simulation example has been performed for two
scenarios, i.e., constant headway and time-varying headway of vehicles. A
performance comparison between the proposed controller and the conventional SMC
was conducted, and controller parameter sensitivity was also carried out. The
simulation results show that the proposed controller is an effective and
ingenious method for platoon system control compared to the conventional sliding
mode controller. Parameter sensitivity analysis shows that only three parameters
need greater attention for maximum convergence rate and disturbance attenuation.
The parameters c, ƞ, and k
can alter the responses of the vehicles.