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

A technology based on neural network and particle swarm optimization, applied in the field of automatic driving vehicle tracking control, can solve the problems of adaptive dynamic change overshoot and instability

Active Publication Date: 2019-02-22
WUHAN UNIV OF TECH
View PDF8 Cites 20 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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 com

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A vehicle speed tracking method based on radial basis function neural network with particle swarm optimization
  • A vehicle speed tracking method based on radial basis function neural network with particle swarm optimization
  • A vehicle speed tracking method based on radial basis function neural network with particle swarm optimization

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0102] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0103] Combine below Figure 1 to Figure 10 Embodiments of the present invention will be described. The specific steps of the embodiment of the present invention are:

[0104] Step 1: Build a vehicle dynamics model through the engine model, transmission system model, vehicle model, and brake model;

[0105] The engine model described in step 1 is:

[0106]

[0107] Among them, T e (t) is the effective torque of the engine at time t, N e (t) is the rotational speed of the crankshaft at time t, A T (t) is the throttle opening at time t, T i (t) is the...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F17/50G06N3/00G06N3/02G05B13/04
CPCG05B13/042G06N3/006G06N3/02G06F30/20
Inventor 尹智帅何嘉雄聂琳真
Owner WUHAN UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap