Search Results for author: Victor M. Preciado

Found 16 papers, 6 papers with code

Parameter-Covariance Maximum Likelihood Estimation

no code implementations15 Nov 2022 Alex Nguyen-Le, Victor M. Preciado

Linear time series modelling is dominated by the use of purely autoregressive models even though incorporating moving average components can greatly improve parsimony.

Time Series Time Series Analysis

Stable and Transferable Hyper-Graph Neural Networks

no code implementations11 Nov 2022 Mikhail Hayhoe, Hans Riess, Victor M. Preciado, Alejandro Ribeiro

To do so, we provide a framework for bounding the stability and transferability error of GNNs across arbitrary graphs via spectral similarity.

Robust Model Predictive Control of Time-Delay Systems through System Level Synthesis

no code implementations23 Sep 2022 Shaoru Chen, Ning-Yuan Li, Victor M. Preciado, Nikolai Matni

In the proposed method, a time-varying feedback control policy is optimized such that the robust satisfaction of all constraints for the closed-loop system is guaranteed.

Model Predictive Control

One-Shot Reachability Analysis of Neural Network Dynamical Systems

no code implementations23 Sep 2022 Shaoru Chen, Victor M. Preciado, Mahyar Fazlyab

The arising application of neural networks (NN) in robotic systems has driven the development of safety verification methods for neural network dynamical systems (NNDS).

Differentiable Safe Controller Design through Control Barrier Functions

no code implementations20 Sep 2022 Shuo Yang, Shaoru Chen, Victor M. Preciado, Rahul Mangharam

Learning-based controllers, such as neural network (NN) controllers, can show high empirical performance but lack formal safety guarantees.

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

2 code implementations21 Mar 2022 Shaoru Chen, Victor M. Preciado, Manfred Morari, Nikolai Matni

However, it is challenging to design LTV state feedback controllers in the face of model uncertainty whose effects are difficult to bound.

Model Predictive Control

Learning Operators with Coupled Attention

1 code implementation4 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.

Model Predictive Control

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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