This paper presents an online evolving neural network-based inverse dynamics learning controller for an autonomous vehicle's longitudinal and lateral control under model uncertainties and disturbances. The inverse dynamics of the vehicle are 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 longitudinal cruise control and a Stanley controller for lateral path-tracking. Performances of both the longitudinal and lateral controllers are compared with existing control methods and the simulation results clearly indicate that the proposed control scheme handles the disturbances and parametric uncertainties better, and also provides better tracking performance in autonomous vehicles.
翻译:本文展示了在模型不确定性和扰动下自动车辆纵向和横向控制的在线进化神经网络反动态动态学习控制器,该飞行器的反动动态使用一个反馈错误学习机制,该机制使用动态半径基功能神经网络,称为扩展最小资源分配网络(EMRAAN)。EMRAAN使用扩大的Kalman过滤器方法进行学习,不断增长/运行的条件有助于尽量减少隐藏神经元的数量。在线学习算法有助于处理不确定性和动态变异以及道路上未知的扰动。拟议的控制结构使用两个同时使用的常规控制器,由EMRAAN反向动态控制器协助。控制结构有一个常规的PID控制器用于纵向巡航航控制,一个斯坦利控制器用于横向跟踪路径。将纵向和横向控制器的性能与现有的控制方法进行比较,模拟结果清楚地表明,拟议的控制机制可以更好地处理扰动和对称不确定性,还可以更好地跟踪自主车辆的性能。