基于神经网络分数阶PI^aD ^β控制的智能车研究

The Intelligent Vehicle Research on Neural Network Fractional Order Control

摘要: 本文将BP神经网络与分数阶有机结合，采用预瞄跟随理论对高度非线性的智能车进行简化建模，设计了基于神经网络分数阶控制的智能车。最后对设计进行了仿真验证，并与普通分数阶及常规PID的控制效果进行了比较分析，仿真验证表明了神经网络分数阶控制器在动态性能、稳态误差等方面都要优于一般分数阶及常规PID控制，证明了本设计的有效性。

Abstract: In this paper, we combined the BP neural network with the fractional order  organically. Using preview follower theory simplified modeling for highly nonlinear intelligent vehicles, designed of fractional-order  control based on neural network intelligent vehicles. Finally, designed with simulation and validation, and with normal fractional order  and conventional PID control effect for a comparative analysis, simulation showed that the fractional-order  controller based on neural networks in terms of dynamic performance, steady-state and error are better than the general fractional order  and conventional PID control, proved the effectiveness of the design.