Data-driven iterative adaptive dynamic programming algorithm for approximate optimal control of unknown nonlinear systems

@article{Li2014DatadrivenIA,
  title={Data-driven iterative adaptive dynamic programming algorithm for approximate optimal control of unknown nonlinear systems},
  author={Hongliang Li and Derong Liu and Ding Wang and Cong Li},
  journal={2014 International Joint Conference on Neural Networks (IJCNN)},
  year={2014},
  pages={3265-3271}
}
In this paper, we develop a data-driven iterative adaptive dynamic programming algorithm to learn offline the approximate optimal control of unknown discrete-time nonlinear systems. We do not use a model network to identify the unknown system, but utilize the available offline data to learn the approximate optimal control directly. First, the data-driven iterative adaptive dynamic programming algorithm is presented with a convergence analysis. Then, the error bounds for this algorithm are… CONTINUE READING

Citations

Publications citing this paper.

References

Publications referenced by this paper.
SHOWING 1-10 OF 32 REFERENCES

Similar Papers

Loading similar papers…