Intelligent agent path tracking control method

Abstract

The invention discloses an intelligent agent path tracking control method, which belongs to the field of path tracking control, and comprises the following steps of identifying a curved path section through which an intelligent agent needs to pass on a target path curve (comprising a path equation and a reference path point sequence) in a current control period, calculating a preview distance ld, positioning a reference path point which is ahead of the intelligent agent and is away from the intelligent agent by ld as a target reference path point, and calculating a deviation weight distance delta1, if deltal is smaller than lmin, the speed difference value deltavr is smaller than deltavrmax, and the rotation angle difference value deltadeltaf is smaller than deltadeltafmax, acquiring a control rotation angle amount delta2 through a model prediction control algorithm, otherwise, obtaining a control rotation angle amount delta1 by using a pure tracking algorithm, according to the transverse position deviation eld of the intelligent agent, calculating a rotation angle correction amount deltadelta, correcting the obtained control rotation angle amount to obtain a control rotation angle amount delta, and sending the control rotation angle amount delta to the intelligent agent to realize path tracking. The lmin, the deltavrmax and the deltadeltafmax are all threshold values. The intelligent agent can move on curve roads with different curvatures, and high-stability and high-precision path tracking is realized.

Description

technical field [0001] The invention belongs to the field of path tracking control, and more particularly relates to an intelligent body path tracking control method. Background technique [0002] Path tracking technology for mobile agents is widely used in many fields such as autonomous AGVs, intelligent driving vehicles, and drones. In the process of collaborative work in industrial working conditions, there are scenarios such as obstacle avoidance, circumvention and lane change curve movement. A single control scheme often cannot meet the requirements of the intelligent body for curve path tracking accuracy and driving stability at the same time. When there is a certain initial position deviation In the case of , the agent will have a heading error that is difficult to converge. At present, scholars at home and abroad have carried out a lot of research work on the stability of agent curve path tracking. [0003] The essence of path tracking control is an optimization pr...

AI Technical Summary

Problems solved by technology

However, the parameters in the traditional MPC control strategy are constant, and when tracking a curved road, the tracking stability and real-time performance of the agent are poor.

[0004] In the prior art, some researches on path tracking control based on model prediction algorithm have been carried out, and the tracking effect can be improved by optimizing the controller parameters and combining other control strategies such as fuzzy logic, but when there is a certain initial deviation When the lower agent is driving on a complex curve with large curvature changes, it is difficult to balance driving stability and path tracking accuracy

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