Name
Abstract | ||
|---|---|---|
In this paper, we propose an optimal control method based on the solution of Hamilton-Jacobi-Bellman (HJB) equation for the continuous-time nonlinear system with bounded unknown perturbation. The robust control system is converted into the corresponding optimal control system with appropriate performance index and the equivalence of the transformation is proved, i.e., the solution of the optimal control problem can globally asymptotically stabilize the robust control system. Adaptive dynamic programming (ADP) based approach is presented to iteratively approximate the optimal performance index and obtain the optimal control policy. A neural network with adaptive weights is applied to implement this approach. An example is given to illustrate the proposed method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IJCNN.2013.6707098 | IJCNN |
Keywords | Field | DocType |
neural network,optimal control,neurocontrollers,adaptive dynamic programming based approach,asymptotic stability,control system synthesis,robust control,optimal control system,robust control system,continuous time systems,nonlinear control systems,adp,adaptive weights,performance index,optimal control method,optimal performance index,transformation equivalence,robust controller design,hamilton-jacobi-bellman equation,dynamic programming,continuous-time nonlinear system,hjb | Hamilton–Jacobi–Bellman equation,Mathematical optimization,Optimal control,Linear-quadratic-Gaussian control,Computer science,Control theory,Automatic control,Exponential stability,Adaptive control,Robust control,Sliding mode control | Conference |
ISSN | ISBN | Citations |
2161-4393 | 978-1-4673-6128-6 | 12 |
PageRank | References | Authors |
0.58 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangnan Zhong | 1 | 346 | 16.35 |
| Haibo He | 2 | 3653 | 213.96 |
| Danil V. Prokhorov | 3 | 374 | 37.68 |