A vehicle speed tracking method based on radial basis function neural network with particle swarm optimization

Abstract

The invention discloses a vehicle speed tracking method of a radial basis function neural network based on particle swarm optimization. The invention constructs an automobile dynamic model through anengine model, a transmission system model, a vehicle model and a brake model. The parameters of radial basis function neural network model are calculated by gradient descent method, and the PID controller adjusts the parameters adaptively by radial basis function neural network model. Parameters of particle swarm optimization are obtained by off-line optimization of particle swarm optimization algorithm. The PSO parameters are initialized and assigned to the radial basis function neural network PID controller. The initial throttle opening or the initial brake pedal position is obtained by theinitialized radial basis function neural network PID controller and input to the vehicle dynamics model to calculate the actual tracking speed. The actual tracking speed and the output of PID controller are inputted into the neural network, and the parameters of RBF neural network and PID controller are adjusted according to the feedback error of the speed. The invention realizes safe and stable tracking target speed.

Description

technical field [0001] The invention belongs to the technical field of automatic driving vehicle tracking control, in particular to a particle swarm optimized radial basis neural network vehicle speed tracking method. Background technique [0002] Vehicle speed tracking is a hot issue in the field of automatic driving. Due to the nonlinear, time-varying and uncertain characteristics of vehicle longitudinal motion, its speed tracking is also a difficult problem in automatic driving vehicle technology. At present, there are many researches on this problem at home and abroad, and many solutions have been proposed, including: adaptive cruise control (ACC), PID control, fuzzy control, synovial structure control and so on. These methods can effectively solve some practical problems, but there are still some deficiencies when applied to the driving of autonomous vehicles in dynamic urban road conditions, such as the inability to adapt well to complex dynamic scenes and parameter un...

AI Technical Summary

Problems solved by technology

[0004]The purpose of the present invention is to solve the problem of overshoot and instability caused by the traditional control method which cannot adapt to the complex scene of dynamic changes well and improper initial parameter selection, and provides A Vehicle Tracking Method with High Accuracy Tracking and Stability Control

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