Name
Abstract | ||
|---|---|---|
Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynamics is developed with the help of policy learning. For addressing the nonaffine nonlinearity, a pre-compensator is constructed, so that the augmented system can be formulated as affine-like form. Different cost functions are defined for original and transformed controlled plants and then their relationship is analyzed in detail. Additionally, an adaptive critic algorithm involving stability guarantee is employed to solve the augmented optimal control problem. At last, several case studies are conducted for verifying the stability, robustness, and optimality of a torsional pendulum plant with suitable cost. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JAS.2019.1911489 | IEEE/CAA Journal of Automatica Sinica |
Keywords | Field | DocType |
Adaptive critic algorithm,learning control,neural approximation,nonaffine dynamics,optimal regulation | Complex system,Approximation algorithm,Optimal control,Nonlinear system,Policy learning,Control theory,Adaptive system,Robustness (computer science),Pendulum,Mathematics | Journal |
Volume | Issue | ISSN |
6 | 3 | 2329-9266 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ding Wang | 1 | 112 | 5.83 |
| Xiangnan Zhong | 2 | 346 | 16.35 |