Search Results for author:
Found 32 papers, 10 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.
Control contraction metrics on Lie groups
The results extend the applicability of the CCM approach and provide a framework for analyzing the behavior of control systems with Lie group structures.
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).
Learning Stable Koopman Embeddings for Identification and Control
Whereas most existing works on Koopman learning do not take into account the stability or stabilizability of the model -- two fundamental pieces of prior knowledge about a given system to be identified -- in this paper, we propose new classes of Koopman models that have built-in guarantees of these properties.
On IMU preintegration: A nonlinear observer viewpoint and its application
The inertial measurement unit (IMU) preintegration approach nowadays is widely used in various robotic applications.
PEBO-SLAM: Observer design for visual inertial SLAM with convergence guarantees
This paper introduces a new linear parameterization to the problem of visual inertial simultaneous localization and mapping (VI-SLAM) -- without any approximation -- for the case only using information from a single monocular camera and an inertial measurement unit.
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.
Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations
We validate the properties of NodeRENs, including the possibility of handling irregularly sampled data, in a case study in nonlinear system identification.
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.
Sparse Resource Allocation for Spreading Processes on Temporal-Switching Networks
Spreading processes, e. g. epidemics, wildfires and rumors, are often modeled on static networks.
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.
Attitude estimation from vector measurements: Necessary and sufficient conditions and convergent observer design
The paper addresses the problem of attitude estimation for rigid bodies using (possibly time-varying) vector measurements, for which we provide a necessary and sufficient condition of distinguishability.
Globally convergent visual-feature range estimation with biased inertial measurements
The design of a globally convergent position observer for feature points from visual information is a challenging problem, especially for the case with only inertial measurements and without assumptions of uniform observability, which remained open for a long time.
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.
Multi-Stage Sparse Resource Allocation for Control of Spreading Processes over Networks
In this paper we propose a method for sparse dynamic allocation of resources to bound the risk of spreading processes, such as epidemics and wildfires, using convex optimization and dynamic programming techniques.
Learning Stable Koopman Embeddings
In this paper, we present a new data-driven method for learning stable models of nonlinear systems.
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.
Distributed Identification of Contracting and/or Monotone Network Dynamics
This paper proposes methods for identification of large-scale networked systems with guarantees that the resulting model will be contracting -- a strong form of nonlinear stability -- and/or monotone, i. e. order relations between states are preserved.
Minimizing the Risk of Spreading Processes via Surveillance Schedules and Sparse Control
Here, risk is considered the risk of an undetected outbreak, i. e. the product of the probability of an outbreak and the impact of that outbreak, and we can bound or minimize the risk by resource allocation and persistent monitoring schedules.
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.
On the equivalence of contraction and Koopman approaches for nonlinear stability and control
control contraction metric) for the nonlinear system.
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.
Sparse Resource Allocation for Control of Spreading Processes via Convex Optimization
In this letter we propose a method for sparse allocation of resources to control spreading processes -- such as epidemics and wildfires -- using convex optimization, in particular exponential cone programming.
Systems and Control Systems and Control Dynamical Systems Optimization and Control
Contracting Implicit Recurrent Neural Networks: Stable Models with Improved Trainability
Stability of recurrent models is closely linked with trainability, generalizability and in some applications, safety.
Specialized Interior Point Algorithm for Stable Nonlinear System Identification
Estimation of nonlinear dynamic models from data poses many challenges, including model instability and non-convexity of long-term simulation fidelity.
Contracting Nonlinear Observers: Convex Optimization and Learning from Data
A new approach to design of nonlinear observers (state estimators) is proposed.
Convex Parameterizations and Fidelity Bounds for Nonlinear Identification and Reduced-Order Modelling
Model instability and poor prediction of long-term behavior are common problems when modeling dynamical systems using nonlinear "black-box" techniques.
