Search Results for author: Victor M. Preciado

Found 11 papers, 5 papers with code

Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesis

1 code implementation21 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.

Learning Operators with Coupled Attention

no code implementations4 Jan 2022 Georgios Kissas, Jacob Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, Paris Perdikaris

Supervised operator learning is an emerging machine learning paradigm with applications to modeling the evolution of spatio-temporal dynamical systems and approximating general black-box relationships between functional data.

Operator learning

System Level Synthesis-based Robust Model Predictive Control through Convex Inner Approximation

1 code implementation10 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.

Learning Region of Attraction for Nonlinear Systems

no code implementations2 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.

AutoEKF: Scalable System Identification for COVID-19 Forecasting from Large-Scale GPS Data

no code implementations28 Jun 2021 Francisco Barreras, Mikhail Hayhoe, Hamed Hassani, Victor M. Preciado

The likelihood of the observations is estimated recursively using an Extended Kalman Filter and can be easily optimized using gradient-based methods to compute maximum likelihood estimators.

Bayesian Inference

Learning Lyapunov Functions for Hybrid Systems

no code implementations22 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

Stabilization of Complementarity Systems via Contact-Aware Controllers

2 code implementations3 Aug 2020 Alp Aydinoglu, Victor M. Preciado, Michael Posa

We propose a control framework which can utilize tactile information by exploiting the complementarity structure of contact dynamics.

Robotics

Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees

no code implementations L4DC 2020 Jacob H. Seidman, Mahyar Fazlyab, Victor M. Preciado, George J. Pappas

By interpreting the min-max problem as an optimal control problem, it has recently been shown that one can exploit the compositional structure of neural networks in the optimization problem to improve the training time significantly.

Robust classification

Contact-Aware Controller Design for Complementarity Systems

2 code implementations24 Sep 2019 Alp Aydinoglu, Victor M. Preciado, Michael Posa

While many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multi-contact motion.

Robotics

Network Design for Controllability Metrics

1 code implementation12 Feb 2019 Cassiano O. Becker, Sérgio Pequito, George J. Pappas, Victor M. Preciado

In this setting, we first consider a feasibility problem consisting of tuning the edge weights such that certain controllability properties are satisfied.

Optimization and Control

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