34 citations · 210 across the 68 of their papers we have counts for
80 papers
Localized Stabilization of Transport PDEs by Interior Flux Feedback
Constantinos Kitsos, Ian R. Manchester
We study stabilization of multidimensional continu- ity equations with source terms on bounded domains by means of localized interior flux feedback. The feedback is prescribed thro…
Robustly Invertible Nonlinear Dynamics and the BiLipREN: From Inversion-Based Control to Generative Trajectory Modelling
Yurui Zhang, Ruigang Wang, Ian R. Manchester
This paper proposes a new notion of robust invertibility for nonlinear dynamical systems, and introduces constructive parameterizations of recurrent neural network which are robust…
Absolute Stability of Nonlinear Negative Imaginary Systems with Application to Potential Energy Shaping
Kanghong Shi, Ian R. Manchester
This paper establishes absolute stability conditions for nonlinear negative imaginary (NI) systems interconnected with static nonlinear feedback. We first show that the NI property…
React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-REN
Nicholas H. Barbara, Ruigang Wang, Alexandre Megretski +1
We study parameterizations of stabilizing nonlinear policies for learning-based control. We propose a structure based on a nonlinear version of the Youla-Kucera parameterization co…
Robustly Invertible Nonlinear Dynamics and the BiLipREN: Contracting Neural Models with Contracting Inverses
Yurui Zhang, Ruigang Wang, Ian R. Manchester
We study the invertibility of nonlinear dynamical systems from the perspective of contraction and incremental stability analysis and propose a new invertible recurrent neural model…
Negative Imaginary Neural ODEs: Learning to Control Mechanical Systems with Stability Guarantees
Kanghong Shi, Ruigang Wang, Ian R. Manchester
We propose a neural control method to provide guaranteed stabilization for mechanical systems using a novel negative imaginary neural ordinary differential equation (NINODE) contro…
R2DN: Scalable Parameterization of Contracting and Lipschitz Recurrent Deep Networks
Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester
This paper presents the Robust Recurrent Deep Network (R2DN), a scalable parameterization of stable and robust recurrent neural networks for machine learning and data-driven contro…
Norm-Bounded Low-Rank Adaptation
Ruigang Wang, Krishnamurthy Dvijotham, Ian R. Manchester
In this work, we propose norm-bounded low-rank adaptation (NB-LoRA) for parameter-efficient fine tuning. NB-LoRA is a novel parameterization of low-rank weight adaptations that adm…
LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers
Patricia Pauli, Ruigang Wang, Ian Manchester +1
We propose a novel layer-wise parameterization for convolutional neural networks (CNNs) that includes built-in robustness guarantees by enforcing a prescribed Lipschitz bound. Each…
On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks
Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester
This paper presents a study of robust policy networks in deep reinforcement learning. We investigate the benefits of policy parameterizations that naturally satisfy constraints on…
Learning Stable and Passive Neural Differential Equations
Jing Cheng, Ruigang Wang, Ian R. Manchester
In this paper, we introduce a novel class of neural differential equation, which are intrinsically Lyapunov stable, exponentially stable or passive. We take a recently proposed Pol…
Control contraction metrics on Lie groups
Dongjun Wu, Bowen Yi, Ian R. Manchester
In this paper, we extend the control contraction metrics (CCM) approach, which was originally proposed for the universal tracking control of nonlinear systems, to those that evolve…
Monotone, Bi-Lipschitz, and Polyak-Lojasiewicz Networks
Ruigang Wang, Krishnamurthy Dvijotham, Ian R. Manchester
This paper presents a new bi-Lipschitz invertible neural network, the BiLipNet, which has the ability to smoothly control both its Lipschitzness (output sensitivity to input pertur…
Learning Stable Koopman Embeddings for Identification and Control
Fletcher Fan, Bowen Yi, David Rye +2
This paper introduces new model parameterizations for learning discrete-time dynamical systems from data via the Koopman operator and studies their properties. Whereas most existin…
On IMU preintegration: A nonlinear observer viewpoint and its application
Bowen Yi, Ian R. Manchester
The inertial measurement unit (IMU) preintegration approach nowadays is widely used in various robotic applications. In this article, we revisit the preintegration theory and propo…
PEBO-SLAM: Observer design for visual inertial SLAM with convergence guarantees
Bowen Yi, Chi Jin, Lei Wang +3
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 o…
RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness
Nicholas H. Barbara, Max Revay, Ruigang Wang +2
Neural networks are typically sensitive to small input perturbations, leading to unexpected or brittle behaviour. We present RobustNeuralNetworks.jl: a Julia package for neural net…
Structured linear quadratic control computations over 2D grids
Armaghan Zafar, Ian R. Manchester
In this paper, we present a structured solver based on the preconditioned conjugate gradient method (PCGM) for solving the linear quadratic (LQ) optimal control problem for $K \tim…
Learning Over Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems (Extended Version)
Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester
This paper presents a policy parameterization for learning-based control on nonlinear, partially-observed dynamical systems. The parameterization is based on a nonlinear version of…
Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations
Daniele Martinelli, Clara Lucía Galimberti, Ian R. Manchester +2
In this work, we introduce and study a class of Deep Neural Networks (DNNs) in continuous-time. The proposed architecture stems from the combination of Neural Ordinary Differential…
Lipschitz-bounded 1D convolutional neural networks using the Cayley transform and the controllability Gramian
Patricia Pauli, Ruigang Wang, Ian R. Manchester +1
We establish a layer-wise parameterization for 1D convolutional neural networks (CNNs) with built-in end-to-end robustness guarantees. In doing so, we use the Lipschitz constant of…
Sparse Resource Allocation for Spreading Processes on Temporal-Switching Networks
Vera L. J. Somers, Ian R. Manchester
Spreading processes, e.g. epidemics, wildfires and rumors, are often modeled on static networks. However, their underlying network structures, e.g. changing contacts in social netw…
Direct Parameterization of Lipschitz-Bounded Deep Networks
Ruigang Wang, Ian R. Manchester
This paper introduces a new parameterization of deep neural networks (both fully-connected and convolutional) with guaranteed Lipschitz bounds, i.e. limited sensitivity to…
Model Predictive Control of Spreading Processes via Sparse Resource Allocation
Ruigang Wang, Armaghan Zafar, Ian R. Manchester
In this paper, we propose a model predictive control (MPC) method for real-time intervention of spreading processes, such as epidemics and wildfire, over large-scale networks. The…