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Found 5 papers, 2 papers with code
Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesis
We propose a novel method for robust model predictive control (MPC) of uncertain systems subject to both polytopic model uncertainty and additive disturbances.
System Level Synthesis-based Robust Model Predictive Control through Convex Inner Approximation
We propose a robust model predictive control (MPC) method for discrete-time linear time-invariant systems with norm-bounded additive disturbances and model uncertainty.
Learning Region of Attraction for Nonlinear Systems
Estimating the region of attraction (ROA) of general nonlinear autonomous systems remains a challenging problem and requires a case-by-case analysis.
DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting
Analyzing the worst-case performance of deep neural networks against input perturbations amounts to solving a large-scale non-convex optimization problem, for which several past works have proposed convex relaxations as a promising alternative.
Learning Lyapunov Functions for Hybrid Systems
By designing the learner and the verifier according to the analytic center cutting-plane method from convex optimization, we show that when the set of Lyapunov functions is full-dimensional in the parameter space, our method finds a Lyapunov function in a finite number of steps.
Optimization and Control
