Learning-Based Adaptive Control for Stochastic Linear Systems with Input Constraints

Seth Siriya, Jingge Zhu, Dragan Nešić, Ye Pu

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.

Knowledge Graph

arrow_drop_up

Comments

Sign up or login to leave a comment