Computing Robust Controlled Invariant Sets of Linear Systems
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Abstract (arXiv)
We consider controllable linear discrete-time systems with bounded perturbations and present two methods to compute robust controlled invariant sets. The first method tolerates an arbitrarily small constraint violation to compute an arbitrarily precise outer approximation of the maximal robust controlled invariant set, while the second method provides an inner approximation. The outer approximation scheme is δ-complete, given that the constraint sets are formulated as finite unions of polytopes.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Jan 2016.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 0; 2025: 0; 2026: 1.
5 papers.
- 2021LTL-Constrained Steady-State Policy Synthesiscited by 2
- 2022Fail-Safe Adversarial Generative Imitation Learningcited by 2
- 2023Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustnesscited by 2
- 2023Control invariant set enhanced reinforcement learning for process control: improved sampling efficiency and guaranteed stabilitycited by 1
- 2026SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Taskscited by 0
- 2026SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks18 Feb 2026
- 2023Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustness24 May 2023
- 2023Control invariant set enhanced reinforcement learning for process control: improved sampling efficiency and guaranteed stability11 Apr 2023
- 2022Fail-Safe Adversarial Generative Imitation Learning3 Mar 2022
- 2021LTL-Constrained Steady-State Policy Synthesis31 May 2021