Name
Abstract | ||
|---|---|---|
The optimal tracking control problem of nonaffine nonlinear discrete-time systems is considered in this paper. The problem relies on the solution of the so-called tracking Hamilton-Jacobi-Bellman equation, which is extremely difficult to be solved even for simple systems. To overcome this difficulty, the data-based Q-learning algorithm is proposed by learning the optimal tracking control policy from data of the practical system. For its implementation purpose, the critic-only neural network structure is developed, where only critic neural network is required to estimate the Q-function and the least-square scheme is employed to update the weight of neural network. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46681-1_68 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Optimal tracking control,Data-based,Q-learning,Critic-only | Nonlinear system,Computer science,Control theory,Q-learning,Artificial intelligence,Discrete time and continuous time,Artificial neural network,Machine learning | Conference |
Volume | ISSN | Citations |
9950 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Biao Luo | 1 | 554 | 23.80 |
| Derong Liu | 2 | 5457 | 286.88 |
| Tingwen Huang | 3 | 5684 | 310.24 |
| Chao Li | 4 | 3 | 1.08 |