郑州大学学报（工学版）2025，Vol.46Issue(3)：11-18,8.DOI:10.13705/j.issn.1671-6833.2025.03.015
基于自适应滑模的四轮移动机器人轨迹跟踪控制
Trajectory Tracking Control of a Four-wheel Mobile Robot Based on Adaptive Sliding Mode Control
摘要
Abstract
This study aimed to address the estimation and compensation of unmatched uncertainties with unknown bounds in sliding mode control.A non-singular terminal sliding mode controller was designed for a four-wheel mo-bile robot.To ensure the existence of the sliding surface,Gaussian process regression(GPR)was employed for on-line estimation of the unmatched uncertainties.GPR not only estimated the bound of the uncertainties but also pro-vided the mean and variance,which allows for a more robust estimation.On the one hand,the use of GPR for un-certainty estimation could help avoid the use of high-gain control,thereby reducing control chattering.On the other hand,the uncertainty compensation based on the estimates from GPR could enhance the adaptability of the model-based sliding mode control algorithm.Additionally,an adaptive terminal sliding mode controller was designed based on the proximal policy optimization(PPO)algorithm.A reward function was constructed with the objective of improving control accuracy and minimizing control input chattering,which could enable the adaptive adjustment of the sliding mode controller's parameters.The stability of the non-singular terminal sliding mode controller was proven through Lyapunov stability analysis.The effectiveness of the proposed control algorithm is validated through numerical simulations.The results demonstrated that the adaptive terminal sliding mode controller based on GPR significantly reduced by 90%while achieving high control accuracy,outperforming traditional control methods.关键词
四轮移动机器人/轨迹跟踪/终端滑模控制/自适应滑模控制/高斯过程回归/强化学习Key words
four-wheel mobile robot/trajectory track/terminal sliding mode control/adaptive sliding mode con-trol/Gaussian process regression/reinforcement learning分类
计算机与自动化引用本文复制引用
郭磊,张雨晴,宋原..基于自适应滑模的四轮移动机器人轨迹跟踪控制[J].郑州大学学报（工学版）,2025,46(3):11-18,8.基金项目
国家自然科学基金资助项目(61105103) （61105103）
