Journal article
Learning-Based Adaptive Control for Stochastic Linear Systems With Input Constraints
S Siriya, J Zhu, D Nesic, Y Pu
IEEE Control Systems Letters | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | Published : 2023
Abstract
We propose a certainty-equivalence scheme for adaptive control of scalar linear systems subject to additive, i.i.d. Gaussian disturbances and bounded control input constraints, without requiring prior knowledge of the bounds of the system parameters, nor the control direction. Assuming that the system is at-worst marginally stable, mean square boundedness of the closed-loop system states is proven. Lastly, numerical examples are presented to illustrate our results.
Grants
Awarded by Australian Research Council
Funding Acknowledgements
The work of Seth Siriya was supported by the Australian Government Research Training Program (RTP) Scholarship. The work of Dragan Nesic was supported by the Australian Research Council through the Discovery Project under Grant DP210102600. The work of Ye Pu was supported by the Australian Research Council under Project DE220101527.