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Found 4 papers, 0 papers with code
Data-Driven Robust Covariance Control for Uncertain Linear Systems
The theory of covariance control and covariance steering (CS) deals with controlling the dispersion of trajectories of a dynamical system, under the implicit assumption that accurate prior knowledge of the system being controlled is available.
Computationally Efficient Chance Constrained Covariance Control with Output Feedback
This paper studies the problem of developing computationally efficient solutions for steering the distribution of the state of a stochastic, linear dynamical system between two boundary Gaussian distributions in the presence of chance-constraints on the state and control input.
Data-Driven Covariance Steering Control Design
This paper studies the problem of steering the distribution of a linear time-invariant system from an initial normal distribution to a terminal normal distribution under no knowledge of the system dynamics.
Probabilistic Verification of ReLU Neural Networks via Characteristic Functions
Verifying the input-output relationships of a neural network so as to achieve some desired performance specification is a difficult, yet important, problem due to the growing ubiquity of neural nets in many engineering applications.
