Path Tracking Method Based on Model Predictive Control and Genetic Algorithm for Autonomous Vehicle

Author:

Wang Meng1ORCID,Chen Juexuan1ORCID,Yang Hanwen2ORCID,Wu Xin1ORCID,Ye Longjiao1ORCID

Affiliation:

1. Wuhan Goyu Intelligent Technology Co., Ltd, Wuhan 430200, China

2. Shanghai JiaoTong University, Shanghai 200240, China

Abstract

Model predictive control (MPC) is often used for controlling the autonomous vehicle tracking the target path. But to apply MPC schemes, the nonlinear model of vehicle kinematics needs to be approximately converted to a linear format, and the path tracking problem has to be converted into certain formats in order to implement the solver of convex quadratic programming. To solve these issues, a control strategy combining MPC and genetic algorithm (GA) is put forward. The nonlinear predictive model is adopted to predict the future movement of a controlled vehicle. The objective function is established according to the future movement and target path. Instead of using a convex quadratic programming solver, GA is applied to solve the optimization problem. The proposed MPC-GA method can handle the arbitrary nonlinear problem and make the objective function more comprehensible and flexible. This method is applied in solving the path tracking problem of an autonomous vehicle. Both simulations and on-field tests are conducted. The results validate the efficiency of the proposed MPC-GA path tracking method in comparison with traditional methods. With the MPC-GA controller, the automatic driving on the park road is basically realized. The control strategy can be considered as an alternative method to solve the path tracking problem for an autonomous vehicle.

Funder

Hubei Key R&D Plan

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

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