Name
Title | ||
|---|---|---|
Output-Feedback Adaptive Neural Controller for Uncertain Pure-Feedback Nonlinear Systems Using a High-Order Sliding Mode Observer. |
Abstract | ||
|---|---|---|
A novel adaptive neural output-feedback controller for SISO nonaffine pure-feedback nonlinear systems is proposed. The majority of the previously described adaptive neural controllers for pure-feedback nonlinear systems were based on the dynamic surface control (DSC) or backstepping schemes. This makes the control law as well as the stability analysis highly lengthy and complicated. Moreover, there has been very limited research till date on the output-feedback neural controller for this class of the systems. The proposed controller evades adopting adaptive backstepping or DSC scheme through reformulating the original system into the Brunovsky form, which considerably simplifies the control law. Combining a high-order sliding mode observer and single radial-basis function network with universal approximation property, it is shown that the controller guarantees closed-loop system stability in the Lyapunov sense. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TNNLS.2018.2861942 | IEEE transactions on neural networks and learning systems |
Keywords | Field | DocType |
Backstepping,Adaptive systems,Observers,Uncertainty,Complexity theory,Artificial neural networks | Lyapunov function,Control theory,Backstepping,Nonlinear system,Pattern recognition,Computer science,Adaptive system,Control theory,Artificial intelligence,Observer (quantum physics),Artificial neural network,Approximation property | Journal |
Volume | Issue | ISSN |
30 | 5 | 2162-2388 |
Citations | PageRank | References |
2 | 0.36 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jang-Hyun Park | 1 | 285 | 17.21 |
| Seong Hwan Kim | 2 | 334 | 31.76 |
| Tae-Sik Park | 3 | 40 | 4.39 |