基于改进大脑情感学习模型的车辆纵向跟随

Vehicle longitudinal following based on improved brain emotional learning model

为提高无人驾驶车辆纵向跟随控制算法的安全性,在大脑情感学习模型的基础上,删除了情感暗示信号,并结合径向基神经网络良好的非线性逼近特性,提出了一种间接自适应脑情绪神经鲁棒控制器(iARBERC)和一种证明该控制系统稳定性的Lyapunov分析方法。将其运用到单级倒立摆仿真中,并与径向基神经网络、ARBENC进行数值比较,仿真结果表明:iARBERC具有最快的响应速度、最小的跟踪误差和最优的鲁棒性能,虽然总控制能量有所提高,但偏差在4%以内。最后,将iARBERC应用到无人驾驶车辆跟随控制系统半物理仿真台架中,结果表明,搭载iARBERC的车辆对频繁变化的纵向速度有较好的跟随能力。

In order to improve the safety of longitudinal follow-up control algorithm for unmanned vehicles,an indirect adaptive brain emotion neural robust controller(iARBERC)was proposed based on the brain emotion learning model and the good nonlinear approximation characteristics of radial basis function neural network.The stability of the control system was proved by Lyapunov analysis method.The simulation results show that iARBERC has the fastest response speed,the smallest tracking error and the best robust performance.Although the total control energy was improved,the deviation is less than 4%.Finally,iARBERC was applied to the semi-physical simulation platform of the following control system of the unmanned vehicle.The results show that the vehicle equipped with iARBERC has good following ability to the frequently changing longitudinal speed.