Data-Driven Adaptive LQR for Completely Unknown LTI Systems

@inproceedings{Jha2017DataDrivenAL,
  title={Data-Driven Adaptive LQR for Completely Unknown LTI Systems},
  author={Sumit Kumar Jha and Sayan Basu Roy and Shubhendu Bhasin},
  year={2017}
}
Abstract In this paper, a data-driven on-policy optimal control design is proposed for continuous-time linear time invariant (LTI) systems with completely unknown dynamics. An online system identifier and control gain parameter estimator, which use past and current data together with standard gradient descent update laws, facilitate the design of an adaptive optimal controller that guarantees parameter convergence without the need of persistence of excitation (PE). Unlike the classical approach… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-5 OF 5 CITATIONS

Data-Driven Adaptive Optimal Tracking Control for Completely Unknown Systems

  • 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)
  • 2018
VIEW 15 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

Memory-Efficient Filter Based Novel Policy Iteration Technique for Adaptive LQR

  • 2018 Annual American Control Conference (ACC)
  • 2018
VIEW 4 EXCERPTS
CITES BACKGROUND & METHODS

Direct Adaptive Optimal Control for Uncertain Continuous-Time LTI Systems Without Persistence of Excitation

  • IEEE Transactions on Circuits and Systems II: Express Briefs
  • 2018
VIEW 2 EXCERPTS
CITES METHODS & BACKGROUND

Policy iteration-based indirect adaptive optimal control for completely unknown continuous-time LTI systems

  • 2017 IEEE Symposium Series on Computational Intelligence (SSCI)
  • 2017
VIEW 2 EXCERPTS
CITES BACKGROUND