Name
Title | ||
|---|---|---|
Adaptive Approximation-Based Regulation Control for a Class of Uncertain Nonlinear Systems Without Feedback Linearizability. |
Abstract | ||
|---|---|---|
In this paper, for a general class of uncertain nonlinear (cascade) systems, including unknown dynamics, which are not feedback linearizable and cannot be solved by existing approaches, an innovative adaptive approximation-based regulation control (AARC) scheme is developed. Within the framework of adding a power integrator (API), by deriving adaptive laws for output weights and prediction error c... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2017.2738918 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Adaptive systems,Vehicle dynamics,Nonlinear dynamical systems,Approximation error,Closed loop systems,Backstepping | Lyapunov function,Mathematical optimization,Backstepping,Nonlinear system,Computer science,Control theory,Adaptive system,Integrator,Vehicle dynamics,Approximation error,Feed forward | Journal |
Volume | Issue | ISSN |
29 | 8 | 2162-237X |
Citations | PageRank | References |
12 | 0.50 | 38 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Wang | 1 | 333 | 18.88 |
| Jing-Chao Sun | 2 | 186 | 6.76 |
| Min Han | 3 | 761 | 68.01 |
| Zhongjiu Zheng | 4 | 12 | 1.51 |
| J. Meng | 5 | 2793 | 174.51 |