A new adaptive sliding mode controller based on the RBF neural network for an electro-hydraulic servo system

被引:112
|
作者
Feng, Hao [1 ]
Song, Qianyu [1 ]
Ma, Shoulei [2 ]
Ma, Wei [2 ]
Yin, Chenbo [2 ]
Cao, Donghui [3 ]
Yu, Hongfu [3 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Artificial Intelligence, Nanjing 210044, Peoples R China
[2] Nanjing Tech Univ, United Inst Excavator Key Technol, Nanjing 211816, Peoples R China
[3] SANY Grp Co Ltd, Suzhou 215300, Peoples R China
基金
中国国家自然科学基金;
关键词
Electro-hydraulic servo system; Sliding mode control; Robotic excavator; RBF neural network; DISTURBANCE REJECTION CONTROL; TRAJECTORY CONTROL; POSITION TRACKING; PID CONTROLLER; DESIGN;
D O I
10.1016/j.isatra.2021.12.044
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Accuracy and robust trajectory tracking for electro-hydraulic servo systems in the presence of load disturbances and model uncertainties are of great importance in many fields. In this work, a new adaptive sliding mode control method based on the RBF neural networks (SMC-RBF) is proposed to improve the performances of a robotic excavator. Model uncertainties and load disturbances of the electro-hydraulic servo system are approximated and compensated using the RBF neural networks. Adaptive mechanisms are designed to adjust the connection weights of the RBF neural networks in real time to guarantee the stability. A nonlinear term is introduced into the sliding mode to design an adaptive terminal sliding mode control structure to improve dynamic performances and the convergence speed. Moreover, a sliding mode chattering reduction method is proposed to suppress the chattering phenomenon. Three types of step, ramp and sine signals are used as the simulation reference trajectories to compare different controllers on a co-simulation platform. Experiments with leveling and triangle conditions are presented on a robotic excavator. Results show that the proposed SMC-RBF controller is superior to existing proportional integral derivative (PID) and sliding mode controller (SMC) in terms of tracking accuracy and disturbance rejection.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.
引用
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页码:472 / 484
页数:13
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