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

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

URL: http://arxiv.org/abs/2209.07040v2
Date: Sat, 17 Sep 2022 03:55:04 GMT
Title: Learning-Based Adaptive Control for Stochastic Linear Systems with Input Constraints
Authors: Seth Siriya, Jingge Zhu, Dragan Ne\v{s}i\'c, Ye Pu
Abstract summary: We propose a certainty-equivalence scheme for adaptive control of scalar linear systems subject to additive, i.i.d. Assuming that the system is at-worst marginally stable, mean square boundedness of the closed-loop system states is proven.
Score: 3.8004168340068336
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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