High Precision Adaptive Robust Neural Network Control of a Servo Pneumatic System

被引:6
作者
Chen, Ye [1 ]
Tao, Guoliang [1 ]
Liu, Hao [1 ]
机构
[1] Zhejiang Univ, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Zhejiang, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2019年 / 9卷 / 17期
基金
中国国家自然科学基金;
关键词
adaptive robust control; neural network; pneumatic systems; PARALLEL MANIPULATOR DRIVEN; NONLINEAR-SYSTEMS; MOTION CONTROL;
D O I
10.3390/app9173472
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
In this paper, an adaptive robust neural network controller (ARNNC) is synthesized for a single-rod pneumatic actuator to achieve high tracking accuracy without knowing the bounds of the parameters and disturbances. The ARNNC control framework integrates adaptive control, robust control, and neural network control intelligently. Adaptive control improves the precision of dynamic compensation with parametric estimation, and robust control attenuates the effect of unmodeled dynamics and unknown disturbances. In reality, the unmodeled dynamics of the complicated pneumatic systems and unpredictable disturbances in working conditions affect the tracking precision. However, these cannot be expressed as an exact formula. Therefore, the real-time learning radial basis function (RBF) neural network component is considered for better compensation of unmodeled dynamics, random disturbances, and estimation errors of the adaptive control. Although the bounds of the parameters and disturbances for the pneumatic systems are unknown, the prescribed transient performance and final tracking accuracy of the proposed method can be still achieved with fictitious bounds. Asymptotic tracking performance can be acquired under the provided circumstance. The comparative experiments with a pneumatic cylinder driven by proportional direction valve illustrate the effectiveness of the proposed ARNNC as shown by a high tracking accuracy is achieved.
引用
收藏
页数:17
相关论文
共 34 条
[1]   Disturbance-Estimated Adaptive Backstepping Sliding Mode Control of a Pneumatic Muscles-Driven Ankle Rehabilitation Robot [J].
Ai, Qingsong ;
Zhu, Chengxiang ;
Zuo, Jie ;
Meng, Wei ;
Liu, Quan ;
Xie, Sheng Q. ;
Yang, Ming .
SENSORS, 2018, 18 (01)
[2]   An Integrated Intelligent Nonlinear Control Method for a Pneumatic Artificial Muscle [J].
Ba, Dang Xuan ;
Dinh, Truong Quang ;
Ahn, Kyoung Kwan .
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2016, 21 (04) :1835-1845
[3]   Robust Adaptive Full-Order TSM Control Based on Neural Network [J].
Cao, Qianlei ;
Cao, Chongzhen ;
Wang, Fengqin ;
Liu, Dan ;
Sun, Hui .
SYMMETRY-BASEL, 2018, 10 (12)
[4]   Reduced-order thermodynamic models for servo-pneumatic actuator chambers [J].
Carneiro, J. Falcao ;
de Almeida, F. Gomes .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 2006, 220 (I4) :301-314
[5]  
Cybenko G., 1989, Mathematics of Control, Signals, and Systems, V2, P303, DOI 10.1007/BF02551274
[6]  
Deyuan Meng, 2011, 2011 International Conference on Fluid Power and Mechatronics, P505, DOI 10.1109/FPM.2011.6045817
[7]   WARD: A pneumatic system for body weight relief in gait rehabilitation [J].
Gazzani, F ;
Fadda, A ;
Torre, M ;
Macellari, V .
IEEE TRANSACTIONS ON REHABILITATION ENGINEERING, 2000, 8 (04) :506-513
[8]   High-Order Sliding-Mode Controllers of an Electropneumatic Actuator: Application to an Aeronautic Benchmark [J].
Girin, Alexis ;
Plestan, Franck ;
Brun, Xavier ;
Glumineau, Alain .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2009, 17 (03) :633-645
[9]   Neural network adaptive robust control with application to precision motion control of linear motors [J].
Gong, JQ ;
Yao, B .
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2001, 15 (08) :837-864
[10]  
Gong JQ, 2001, P AMER CONTR CONF, P3533, DOI 10.1109/ACC.2001.946180