mlsanityData to 13 Oct 2026

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

arXiv 2209.07040 ↗First posted 15 Sep 2022eess.SY cs.LG cs.SYcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 7 of its 30 references that are in the map. First posted on arXiv 15 Sep 2022.

Builds on

7 of 30 references.

References · 7

  1. 2018Learning Without Mixing: Towards A Sharp Analysis of Linear System Identificationcited by 158
  2. 2020Naive Exploration is Optimal for Online LQRcited by 111
  3. 2018Safely Learning to Control the Constrained Linear Quadratic Regulatorcited by 31
  4. 2020Reinforcement Learning with Fast Stabilization in Linear Dynamical Systemscited by 20
  5. 2021Safe Adaptive Learning-based Control for Constrained Linear Quadratic Regulators with Regret Guaranteescited by 10
  6. 2022Thompson Sampling Achieves tilde O(√T) Regret in Linear Quadratic Controlcited by 8
  7. 2018Output feedback stable stochastic predictive control with hard control constraintscited by 1

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2023Stability Bounds for Learning-Based Adaptive Control of Discrete-Time Multi-Dimensional Stochastic Linear Systems with Input Constraintscited by 2
  2. 2024Non-Asymptotic Bounds for Closed-Loop Identification of Sub-Exponentially Growing Nonlinear Stochastic Systemscited by 0
  3. 2025A Framework for Adaptive Stabilisation of Nonlinear Stochastic Systemscited by 0