[1]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(03):11-18.[doi:10.13705/j.issn.1671-6833.2025.03.015]
　GUO Lei,ZHANG Yuqing,SONG Yuan.Trajectory Tracking Control of a Four-wheel Mobile Robot Based on Adaptive Sliding Mode Control[J].Journal of Zhengzhou University (Engineering Science),2025,46(03):11-18.[doi:10.13705/j.issn.1671-6833.2025.03.015]
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基于自适应滑模的四轮移动机器人轨迹跟踪控制()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年03期
页码:
11-18
栏目:
出版日期:
2025-05-13

文章信息/Info

Title:
Trajectory Tracking Control of a Four-wheel Mobile Robot Based on Adaptive Sliding Mode Control
文章编号:
1671-6833(2025)03-0011-08
作者:
郭　磊1;  张雨晴2;  宋　原1
1.北京邮电大学 智能工程与自动化学院,北京 100876;2.北京邮电大学 人工智能学院,北京 100876
Author(s):
GUO Lei1;  ZHANG Yuqing2;  SONG Yuan1
1.School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China; 2.School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
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
Keywords:
four-wheel mobile robot;  trajectory track;  terminal sliding mode control;  adaptive sliding mode control;  Gaussian process regression;  reinforcement learning
分类号:
TP13 TP242.6
DOI:
10.13705/j.issn.1671-6833.2025.03.015
文献标志码:
A
摘要:
针对滑模控制中具有未知界的非匹配不确定性的估计与补偿问题,提出了一种适用于四轮移动机器人的非奇异终端滑模控制器,不需要已知界的不确定性的先验知识。为了保证滑模面的存在性,引入高斯过程回归对非匹配不确定性进行在线估计,一方面避免采用高增益的控制,从而减小了控制量抖振;另一方面,通过高斯过程回归的估计结果对系统的不确定性进行补偿,从而提高了基于模型的滑模控制器的适应性。基于近端策略优化 (PPO)算法设计了一种自适应终端滑模控制器,通过控制精度和控制输入的抖振幅度来构建奖励函数,以此对滑模控制器的参数进行自适应调整,从而减小抖振并提高跟踪精度。通过李雅普诺夫稳定性分析证明了非奇异终端滑模控制器的稳定性,基于数值仿真实验验证了所设计控制器的有效性。结果表明:与传统的终端滑模相比,所提出的自适应终端滑模控制器在保持较高控制精度的同时,抖振振幅减小了90%,优于传统的控制方法。
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 mobile robot. To ensure the existence of the sliding surface, Gaussian process regression (GPR) was employed for online estimation of the unmatched uncertainties. GPR not only estimated the bound of the uncertainties but also provided the mean and variance, which allows for a more robust estimation. On the one hand, the use of GPR for uncertainty 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 modelbased 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.

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更新日期/Last Update: 2025-05-22