Search Results for author:
Found 16 papers, 6 papers with code
Learning Stable and Passive Neural Differential Equations
We take a recently proposed Polyak Lojasiewicz network (PLNet) as an Lyapunov function and then parameterize the vector field as the descent directions of the Lyapunov function.
Monotone, Bi-Lipschitz, and Polyak-Lojasiewicz Networks
This paper presents a new \emph{bi-Lipschitz} invertible neural network, the BiLipNet, which has the ability to control both its \emph{Lipschitzness} (output sensitivity to input perturbations) and \emph{inverse Lipschitzness} (input distinguishability from different outputs).
RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness
Neural networks are typically sensitive to small input perturbations, leading to unexpected or brittle behaviour.
Learning Over Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems (Extended Version)
This paper presents a policy parameterization for learning-based control on nonlinear, partially-observed dynamical systems.
Learning Stable and Robust Linear Parameter-Varying State-Space Models
This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models.
Lipschitz-bounded 1D convolutional neural networks using the Cayley transform and the controllability Gramian
We establish a layer-wise parameterization for 1D convolutional neural networks (CNNs) with built-in end-to-end robustness guarantees.
Direct Parameterization of Lipschitz-Bounded Deep Networks
This paper introduces a new parameterization of deep neural networks (both fully-connected and convolutional) with guaranteed $\ell^2$ Lipschitz bounds, i. e. limited sensitivity to input perturbations.
Learning over All Stabilizing Nonlinear Controllers for a Partially-Observed Linear System
This paper proposes a nonlinear policy architecture for control of partially-observed linear dynamical systems providing built-in closed-loop stability guarantees.
Youla-REN: Learning Nonlinear Feedback Policies with Robust Stability Guarantees
This paper presents a parameterization of nonlinear controllers for uncertain systems building on a recently developed neural network architecture, called the recurrent equilibrium network (REN), and a nonlinear version of the Youla parameterization.
Contraction-Based Methods for Stable Identification and Robust Machine Learning: a Tutorial
This tutorial paper provides an introduction to recently developed tools for machine learning, especially learning dynamical systems (system identification), with stability and robustness constraints.
Recurrent Equilibrium Networks: Flexible Dynamic Models with Guaranteed Stability and Robustness
RENs are otherwise very flexible: they can represent all stable linear systems, all previously-known sets of contracting recurrent neural networks and echo state networks, all deep feedforward neural networks, and all stable Wiener/Hammerstein models, and can approximate all fading-memory and contracting nonlinear systems.
Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics
In this paper, we present a virtual control contraction metric (VCCM) based nonlinear parameter-varying (NPV) approach to design a state-feedback controller for a control moment gyroscope (CMG) to track a user-defined trajectory set.
Lipschitz-Bounded Equilibrium Networks
In image classification experiments we show that the Lipschitz bounds are very accurate and improve robustness to adversarial attacks.
Lipschitz Bounded Equilibrium Networks
In image classification experiments we show that the Lipschitz bounds are very accurate and improve robustness to adversarial attacks.
A Convex Parameterization of Robust Recurrent Neural Networks
Recurrent neural networks (RNNs) are a class of nonlinear dynamical systems often used to model sequence-to-sequence maps.
Virtual Control Contraction Metrics: Convex Nonlinear Feedback Design via Behavioral Embedding
This paper presents a systematic approach to nonlinear state-feedback control design that has three main advantages: (i) it ensures exponential stability and $ \mathcal{L}_2 $-gain performance with respect to a user-defined set of reference trajectories, and (ii) it provides constructive conditions based on convex optimization and a path-integral-based control realization, and (iii) it is less restrictive than previous similar approaches.
