This paper presents an online evolving neural network-based inverse dynamics learning controller for an autonomous vehicles' longitudinal and lateral control under model uncertainties and disturbances. The inverse dynamics of the vehicle is approximated using a feedback error learning mechanism that utilizes a dynamic Radial Basis Function neural network, referred to as the Extended Minimal Resource Allocating Network (EMRAN). EMRAN uses an extended Kalman filter approach for learning and a growing/pruning condition helps in keeping the number of hidden neurons minimum. The online learning algorithm helps in handling the uncertainties and dynamic variations and also the unknown disturbances on the road. The proposed control architecture employs two coupled conventional controllers aided by the EMRAN inverse dynamics controller. The control architecture has a conventional PID controller for cruise control and a Stanley controller for path-tracking. Performances of both the longitudinal and lateral controllers are compared with existing control methods and the results clearly indicate that the proposed control scheme handles the disturbances and parametric uncertainties better, and also provides better tracking performance in autonomous vehicles.

中文翻译：

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用于自动驾驶汽车的基于 EMRAN 的鲁棒神经辅助学习控制器

本文提出了一种基于在线演化神经网络的逆动力学学习控制器，用于在模型不确定性和干扰下自动驾驶车辆的纵向和横向控制。使用反馈误差学习机制近似车辆的逆动力学，该机制利用动态径向基函数神经网络，称为扩展最小资源分配网络 (EMRAN)。EMRAN 使用扩展卡尔曼滤波器方法进行学习，增长/修剪条件有助于将隐藏神经元的数量保持在最低限度。在线学习算法有助于处理道路上的不确定性和动态变化以及未知的干扰。所提出的控制架构采用两个耦合的传统控制器，由 EMRAN 逆动态控制器辅助。控制架构具有用于巡航控制的传统 PID 控制器和用于路径跟踪的 Stanley 控制器。将纵向和横向控制器的性能与现有控制方法进行了比较，结果清楚地表明，所提出的控制方案可以更好地处理干扰和参数不确定性，并且在自动驾驶汽车中也提供了更好的跟踪性能。