基于自适应RBF 网络补偿的智能车辆循迹控制

针对智能车辆这一复杂非线性时变系统的循迹控制问题,提出一种基于Lyapunov函数方法的RBF神经网络自适应补偿控制策略.首先建立了车辆循迹控制的动力学名义模型;然后利用RBF神经网络对车辆循迹控制名义模型的不精确部分进行自适应补偿;最后应用Lyapunov稳定性理论推导出RBF网络权值的训练规则并证明了控制系统的稳定性.仿真结果表明,该方法提高了循迹控制的精度,具有较高的可行性和实用性.

Intelligent vehicle’s path tracking control based on self-adaptive RBF network compensation

A self-adaptive RBF neuron network compensation control strategy based on the Lyapunov function is proposed in order to solve the path tracking problem of intelligent vehicles which is much complicated with nonlinear and time-varying characteristics. Firstly, the nominal dynamic model of vehicle’s path tracking is built. Then, RBF neuron network is used to compensate this nominal model’s inaccuracy parts. Finally, the learning rule is obtained based on the Lyapunov function, and the stability of this system is proved at the same time. The simulation results show that this strategy is much more accurate and with higher feasibility and practicability.