Persistency of Excitation and Performance of Deterministic Learning
Recently,a deterministic learning theory was proposed for locally-accurate identi.cation of nonlinear systems. In this paper,we investigate the performance of deterministic learning,including the learning speed and learning accuracy. By analyzing the convergence properties of a class of linear time-varying (LTV) systems,explicit relations between the persistency of excitation (PE) condition (especially the level of excitation) and the convergence properties of the LTV systems are derived. It is shown that the learning speed increases with the level of excitation and decreases with the upper bound of PE. The learning accuracy also increases with the level of excitation,in particular,when the level of excitation is large enough,locally-accurate learning can be achieved to the desired accuracy,whereas low level of PE may result in the deterioration of the learning performance. This paper reveals that the performance analysis of deterministic learning can be established on the basis of classical results on stability and convergence of adaptive control. Simulation studies on the Moore-Greitzer model,a well-known axial.ow compressor model,are included to illustrate the effectiveness of the results.
YUAN Chengzhi WANG Cong
College of Automation and the Center for Control and Optimization,South China University of Technology,Guangzhou 510640,P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-8
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）