Deterministic Learning of a Class of Nonlinear Systems with Relaxed Conditions
A deterministic learning theory was recently presented which states that all appropriately designed adaptive neural controller can learn the system internal dynamics while attempting to control a class of simple nonlinear systems in Brunovsky form. In this paper,we investigate deterministic learning from adaptive neural control of a class of nonlinear systems with mild assumptions on the lower and upper bounds of af.ne term g(x). To overcome the dif.culties brought by the af.ne terms for learning,.rstly,the tracking control and the stability of the closed-loop system are guaranteed by the use of ISS (Input-to-State Stability) analysis and SG (Small Gain) theorem. Secondly,without bound of the derivative of af.ne term,deterministic learning of the unknown system dynamics can be implemented when the exponential stability of a class of LTV systems is achieved. In addition,the utilization of knowledge learned is also investigated,i.e.,a non-high gains controller is constructed to improve the control performance and reduce the control cost.
WEN Binhe WANG Cong
College of Automation Science and Engineering,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
1-6
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）