Name
Title | ||
|---|---|---|
Robust output feedback control of nonlinear stochastic systems using neural networks. |
Abstract | ||
|---|---|---|
We present an adaptive output feedback controller for a class of uncertain stochastic nonlinear systems. The plant dynamics is represented as a nominal linear system plus nonlinearities. In turn, these nonlinearities are decomposed into a part, obtained as the best approximation given by neural networks, plus a remaining part which is treated as uncertainties, modeling approximation errors, and neglected dynamics. The weights of the neural network are tuned adaptively by a Lyapunov design. The proposed controller is obtained through robust optimal design and combines together parameter projection, control saturation, and high-gain observers. High performances are obtained in terms of large errors tolerance as shown through simulations. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/TNN.2002.806609 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
neural network,robust optimal design,remaining part,best approximation,control saturation,lyapunov design,proposed controller,adaptive output feedback controller,approximation error,nonlinear stochastic system,robust output feedback control,large errors tolerance,linear system,asymptotic stability,neural nets,approximation theory,robust control,control systems,feedback,optimal control,adaptive control,nonlinear system,neural networks | Lyapunov function,Control theory,Optimal control,Nonlinear system,Linear system,Computer science,Control theory,Approximation theory,Artificial neural network,Robust control | Journal |
Volume | Issue | ISSN |
14 | 1 | 1045-9227 |
Citations | PageRank | References |
7 | 1.12 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefano Battilotti | 1 | 136 | 42.34 |
| Alfredo De Santis | 2 | 4049 | 501.27 |