Anticipation-Based Autonomous Platoon Control Strategy with Minimum Parameter Learning Adaptive Radial Basis Function Neural Network Sliding Mode Control

Natnael M. Negash; James Yang

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Anticipation-Based Autonomous Platoon Control Strategy with Minimum Parameter Learning Adaptive Radial Basis Function Neural Network Sliding Mode Control

机译：基于预测的最小参数学习自主排控制策略自适应径向基函数神经网络滑模控制

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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, η, and k can alter the responses of the vehicles.

机译：本文调查进展和最优速度跟踪的自动车辆(AVs),考虑到他们预测的驾驶空间的稳定性和完整性排的形成。预期用于车辆模型自治系统建模。自适应径向基函数神经网络(ARBF-NN)的滑模控制(SMC)提出了控制目的。目标是调节流量扰动在整个道路的操作。控制器经验较少的计算负担和适应复杂性,最低参数学习(MPL)也被整合ARBF-NN-based SMC。仿真已经完成了两个例子和场景,即不断进展时变进展的车辆。提出的控制器和之间的比较传统SMC,和控制器参数的敏感性也进行出去了。提出的控制器是一种有效的和巧妙的排系统控制方法相比传统的滑模控制器。敏感性分析表明,只有三个参数需要更大的最大关注收敛速度和扰动衰减。参数c,η,和k可以改变的反应的车辆。

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《SAE International Journal of Vehicle Dynamics, Stability, and NVH》 |2022年第3期|247-266|共20页
作者
Natnael M. Negash; James Yang;
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Texas Tech University;

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正文语种英语
中图分类汽车发动机;
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Anticipation-Based Autonomous Platoon Control Strategy with Minimum Parameter Learning Adaptive Radial Basis Function Neural Network Sliding Mode Control

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