Linear Quadratic Tracking Control of Partially-Unknown Continuous-Time Systems Using Reinforcement Learning

@article{Modares2014LinearQT,
  title={Linear Quadratic Tracking Control of Partially-Unknown Continuous-Time Systems Using Reinforcement Learning},
  author={Hamidreza Modares and Frank L. Lewis},
  journal={IEEE Transactions on Automatic Control},
  year={2014},
  volume={59},
  pages={3051-3056}
}
In this technical note, an online learning algorithm is developed to solve the linear quadratic tracking (LQT) problem for partially-unknown continuous-time systems. It is shown that the value function is quadratic in terms of the state of the system and the command generator. Based on this quadratic form, an LQT Bellman equation and an LQT algebraic Riccati equation (ARE) are derived to solve the LQT problem. The integral reinforcement learning technique is used to find the solution to the LQT… CONTINUE READING
Highly Cited
This paper has 281 citations. REVIEW CITATIONS

3 Figures & Tables

Topics

Statistics

05010020142015201620172018
Citations per Year

282 Citations

Semantic Scholar estimates that this publication has 282 citations based on the available data.

See our FAQ for additional information.