A Hybrid Control Scheme Based on Neural Networks for Servo System
In this paper, a novel hybrid control scheme based on RBF Neural Network (RBFNN) is proposed for servo system to achieve high tracking precision. This hybrid control scheme consists of a three-layer RBFNN Controller, a Feedforward controller and a Feedback controller. The RBFNN controller is introduced to reduce influence which arose from modeling error, unknown model dynamics, parameter variation and disturbance acted on the system. All adaptive learning algorithms of the RBFNN’s weights are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop whether the uncertainties occur or not. Experiment results on 3-axis flying simulator verify the proposed strategy can achieve high tracking precision for real-time position close-loop servo system. Besides, the RBFNN controller also extends the bandwidth of the system.
RBFNN Servo system Lyapunov stability
Hu Hongjie Kang Zhao Kou Peng Yue Jinyu
School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100191, P.R. China Beijing Institute of Radio Metrology and MeasurementBeijing 100854, P.R. China School of Automation Science and Electrical Engineering Beihang University Beijing 100191, P.R. Chin
国际会议
哈尔滨
英文
207-212
2011-01-18（万方平台首次上网日期，不代表论文的发表时间）