Name
Title | ||
|---|---|---|
Barrier Lyapunov function-based tracking control for stochastic nonlinear systems with full-state constraints and input saturation |
Abstract | ||
|---|---|---|
This paper investigates the adaptive tracking control problem of stochastic nonlinear systems under the conditions of full-state constraints and input saturation. The barrier Lyapunov function (BLF) is applied to handle the full-state constraints. To deal with the input saturation, a distinctive method of introducing an auxiliary system is adopted. Then, a systematic controller design procedure is given by combining a novel radial basis function neural network (RBF NN) approximation approach with backstepping technique.By this way, an adaptive state-feedback controller with only one adaptive law is obtained, which renders the closed-loop system semi-globally uniformly ultimately bounded. Meanwhile, the tracking error is bounded by an explicit function of the design parameters and saturated input error. In addition, the full-states are not violated. Finally, a simple pendulum system and a numerical example are simulated to demonstrate the effectiveness of the proposed scheme. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.jfranklin.2020.09.022 | Journal of the Franklin Institute |
DocType | Volume | Issue |
Journal | 357 | 17 |
ISSN | Citations | PageRank |
0016-0032 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huifang Min | 1 | 47 | 9.39 |
| Na Duan | 2 | 262 | 12.56 |
| Shengyuan Xu | 3 | 1105 | 60.42 |
| Shu-Min Fei | 4 | 1150 | 96.93 |