Data-Based Adaptive Output Feedback Tracking Control for a Class of Nonlinear Systems

被引：0
|
作者：
Ren, Ling [1 ]
Zhang, Guoshan [1 ]
机构：
[1] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
来源：
PROCEEDINGS OF 2018 IEEE 7TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS) | 2018年
关键词：
Adaptive RBFNN Observer; Online Learning; Output Feedback Tracking Control; Sliding Mode Control; SLIDING MODE CONTROL; OBSERVER; ROBUST;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, we propose an output feedback tracking control scheme for a class of continuous-time nonlinear systems without specific model. A radial basis function neural network (RBFNN) observer is designed to online estimate the unmeasured inner state variables only using the input and output data. Based on the designed RBFNN observer, a sliding mode controller is derived to guarantee that the system states follow the desired trajectories. Simulation results on an example show the effectiveness and tracking performance of the proposed scheme.
引用
收藏
页码：246 / 250
页数：5
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