Aug 21, 2020 · Abstract: This article investigates stochastic invariance for control systems through probabilistic controlled invariant sets (PCISs).
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Jul 5, 2021 · Abstract—This paper investigates stochastic invariance for control systems through probabilistic controlled invariant sets. (PCISs).
This paper investigates stochastic invariance for control systems through probabilistic controlled invariant sets (PCISs). As a natural complement to robust ...
We develop a method for computing controlled invariant sets of discrete-time affine systems using Sum-of-Squares programming.
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Jul 15, 2024 · We compute probabilistic controlled invariant sets for nonlinear systems using Gaussian process state space models, which are data-driven models.
The concept of probabilistic invariance was introduced to extend the widely applied concept of invariance to this class of problems.
As the main contribution, we use the interval analysis method to iteratively computing the inner-approximation of a controlled -invariant set, which is a key ...
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Abstract. Gaussian process state space models are becoming common tools for the analysis and design of nonlinear systems with uncertain dynamics.
The concept of probabilistic invariance was introduced to extend the widely applied concept of invariance to this class of problems. Computational methods for ...
This work presents a data-driven method for approximation of the maximum positively invariant (MPI) set and the maximum controlled invariant (MCI) set for ...