Deterministic Learning from NN Output Feedback Control of Brunovsky Systems
Recently,a deterministic learning theory was presented in which an appropriately designed adaptive neural controller can learn the system internal dynamics while attempting to control a class of nonlinear systems. In this paper,we investigate deterministic learning from adaptive neural network (NN) control of Brunovsky systems by using only output measurements. A high-gain observer (HGO) is.rst adopted to accurately estimate the derivatives of the system output. Then,an adaptive output feedback NN controller is proposed to guarantee that the output tracking error is small and bounded. The dif.culty caused by the unknown af.ne term in deterministic learning is analyzed,the measures to eliminate peaking phenomenon associated with the use of HGO is proposed. When a partial persistent excitation (PE) condition is satis.ed,tracking to an output recurrent reference trajectory and the exponential stability of the linear time-varying (LTV) system can be guaranteed. Locally accurate identi.cation of the unknown closed-loop system dynamics can be achieved along a periodic orbit of closed-loop estimated signals. Consequently,learning from NN output feedback control of Brunovsky system is implemented. A neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve the well output tracking performance. It simpli.es the controller design,saves the computing time and decreases amounts of output actuators in practical implementation.
ZENG Wei WANG Cong
College of Automation Science and Engineering,South China University of Technology,Guangzhou 510640, College of Automation Science and Engineering,South China University of Technology,Guangzhou 510640,
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）