Adaptive sliding mode and safety control for excavators using Kinematic Control Barrier Function and sliding mode disturbance observer

被引:1
作者
Huang, Weidi [1 ]
Wang, Qi [1 ]
Yang, Shuwei [1 ]
Zhang, Junhui [1 ]
Xu, Bing [1 ]
机构
[1] Zhejiang Univ, State Key Lab Fluid Power & Mech Syst, Hangzhou 310027, Peoples R China
关键词
Excavator; Control barrier functions; Adaptive sliding mode control; Barrier functions; Sliding mode disturbance observer; TRACKING CONTROL; ROBOTIC SYSTEM; MOTION CONTROL; MOTOR;
D O I
10.1016/j.autcon.2025.106046
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Excavators have complex structures, large load, and often works in scenarios with safety hazards. Existing methods overlook control-level safety and over-prioritize accuracy, neglecting input smoothness. To address these challenges, a Barrier Functions Adaptive Sliding Mode (BFASM) safety control method based on Kinematic Control Barrier Function (KCBF) and Sliding Mode Disturbance Observer (SMDO) is proposed. Specifically, a virtual motion trajectory tracking controller is established and CBF provides safe joint velocity inputs for system. A Barrier Function (BF)-based anti-saturation adaptive sliding mode controller is proposed. SMDO is used to estimate the lumped disturbance. BF is used to design the bounded adaptive control gain to ensure that sliding variables remain in predefined neighborhoods of zero, and its size does not depend on disturbance boundary. Simulation results demonstrate that the proposed safety controller effectively ensures safety and the tracking controller keeps errors within a predefined range of 1 degrees with no chattering of control inputs.
引用
收藏
页数:13
相关论文
共 60 条
[1]  
Ames AD, 2019, 2019 18TH EUROPEAN CONTROL CONFERENCE (ECC), P3420, DOI [10.23919/ecc.2019.8796030, 10.23919/ECC.2019.8796030]
[2]  
Black M, 2021, 2021 EUROPEAN CONTROL CONFERENCE (ECC), P1328, DOI 10.23919/ECC54610.0000/2021.9655080
[3]   Comparison of online and offline deep reinforcement learning with model predictive control for thermal energy management [J].
Brandi, Silvio ;
Fiorentini, Massimo ;
Capozzoli, Alfonso .
AUTOMATION IN CONSTRUCTION, 2022, 135
[4]   Robust MPC for LPV systems via a novel optimization-based constraint tightening [J].
Bujarbaruah, Monimoy ;
Rosolia, Ugo ;
Sturz, Yvonne R. ;
Zhang, Xiaojing ;
Borrelli, Francesco .
AUTOMATICA, 2022, 143
[5]   A nonlinear disturbance observer for robotic manipulators [J].
Chen, WH ;
Ballance, DJ ;
Gawthrop, PJ ;
O'Reilly, J .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2000, 47 (04) :932-938
[6]   High accuracy contouring control of an excavator for surface flattening tasks based on extended state observer and task coordinate frame approach [J].
Dao, Hoang Vu ;
Na, Seonjun ;
Nguyen, Duc Giap ;
Ahn, Kyoung Kwan .
AUTOMATION IN CONSTRUCTION, 2021, 130
[7]   Physics-informed neutral network with physically consistent and residual learning for excavator precision operation control [J].
Feng, Chenlong ;
Wang, Jixin ;
Shen, Yuying ;
Wang, Qi ;
Xiong, Yi ;
Zhang, Xudong ;
Fan, Jiuchen .
APPLIED SOFT COMPUTING, 2024, 167
[8]   Task-unit based trajectory generation for excavators utilizing expert operator skills [J].
Feng, Chenlong ;
Shen, Yuying ;
Wang, Jixin ;
Wang, Qi ;
Suo, Zhe ;
Su, Fa .
AUTOMATION IN CONSTRUCTION, 2024, 158
[9]   Safety and Efficiency in Robotics: The Control Barrier Functions Approach [J].
Ferraguti, Federica ;
Talignani Landi, Chiara ;
Singletary, Andrew ;
Lin, Hsien-Chung ;
Ames, Aaron ;
Secchi, Cristian ;
Bonfe, Marcello .
IEEE ROBOTICS & AUTOMATION MAGAZINE, 2022, 29 (03) :139-151
[10]   Robust sliding mode control of discrete fractional difference chaotic system [J].
Fu, Hui ;
Kao, Yonggui .
NONLINEAR DYNAMICS, 2025, 113 (02) :1419-1431