Integration of Artificial Neural Networks and Linear Systems for the Output Feedback Control of Nonlinear Vibration Systems
This paper analyzes the integration of neural networks and linear systems for the identification,state estimation and output feedback control of weakly nonlinear systems.Considering previous knowledge about the system given by approximated linear state-space models,linear observers and linear controllers,training algorithms for the neuro-identification,state neuro-estimation and output feedback neuro-control were derived considering the dynamics of the nonlinear system.It was found that the integrated linear-neuro model can identify the dynamics of the system much more accurately than a purely linear model or a purely neuro model.It was also found that the state estimation and vibration isolation performance of the system with integrated linear-neuro output feedback control is better than the system with linear control or neuro-control.
Artificial Neural Networks Linear Systems Control Theory Neuro-Control
Javier G.Rázuri Antonio M.Cardenas Rahim Rahmani David Sundgren Ikuo Mizuuchi
DSV Department,Stockholm University EED Department,Pontifical Catholic University of Perú MSE Department,Tokyo University of Agriculture and Technology
国际会议
The 33th Chinese Control Conference第33届中国控制会议
南京
英文
1850-1855
2014-07-28(万方平台首次上网日期,不代表论文的发表时间)