Trajectory tracking of mobile robots based on parameter estimation and primal-dual neural network predictive control
ZHANG Lang-wen
WANG Zhong-xu
WEI Hai-xiang
XIE Wei
Abstract：This work focuses on the problem of uncertain parameter estimation and trajectory tracking for wheeled mobile robots.A method for estimating uncertain model parameters of mobile robots based on the convolutional neural network(CNN)is studied,and a primal-dual neural network(PDNN)model predictive control(MPC)tracking control algorithm for mobile robots is proposed.For wheeled mobile robots,tire lateral stiffness is affected by load disturbance,unmodelled dynamics and load changes,which is difficult to measure in real time during actual driving.CNN estimator of lateral stiffness is designed to eliminate uncertainty during robot operation considering the constraint conditions of front wheel deviation and acceleration.This work studies the design of predictive control for mobile robots based on CNN parameter estimation and proposes a PDNN based algorithm with CNN parameter estimation for solving the predictive control problem of mobile robots.The stability of the proposed PDNN-MPC algorithm is proved.Finally,to verify the effectiveness of the controller,the proposed PDNN-MPC algorithm is validated.
Keywords：mobile robotstrajectory trackingmodel predictive controlprimal-dual neural networkconvolutional neural network
Publication Date：2026-02-28
Online Publishing Date：2026-04-08(First online date of this platform, not the publication date of the document)
Pages：9( 278-286 )