1 code implementation • 21 Mar 2022 • Shaoru Chen, Victor M. Preciado, Manfred Morari, Nikolai Matni
We propose a novel method for robust model predictive control (MPC) of uncertain systems subject to both polytopic model uncertainty and additive disturbances.
1 code implementation • 10 Nov 2021 • Shaoru Chen, Nikolai Matni, Manfred Morari, Victor M. Preciado
We propose a robust model predictive control (MPC) method for discrete-time linear time-invariant systems with norm-bounded additive disturbances and model uncertainty.
no code implementations • 2 Oct 2021 • Shaoru Chen, Mahyar Fazlyab, Manfred Morari, George J. Pappas, Victor M. Preciado
Estimating the region of attraction (ROA) of general nonlinear autonomous systems remains a challenging problem and requires a case-by-case analysis.
no code implementations • 16 Jun 2021 • Shaoru Chen, Eric Wong, J. Zico Kolter, Mahyar Fazlyab
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.
no code implementations • 22 Dec 2020 • Shaoru Chen, Mahyar Fazlyab, Manfred Morari, George J. Pappas, Victor M. Preciado
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