34 citations · 210 across the 68 of their papers we have counts for
45 papers · 1 filter
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…
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…
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…
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…
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…
Attitude estimation from vector measurements: Necessary and sufficient conditions and convergent observer design
Bowen Yi, Lei Wang, Ian R. Manchester
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…
Youla-REN: Learning Nonlinear Feedback Policies with Robust Stability Guarantees
Ruigang Wang, Ian R. Manchester
This paper presents a parameterization of nonlinear controllers for uncertain systems building on a recently developed neural network architecture, called the recurrent equilibrium…
Globally convergent visual-feature range estimation with biased inertial measurements
Bowen Yi, Chi Jin, Ian R. Manchester
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 a…
Learning over All Stabilizing Nonlinear Controllers for a Partially-Observed Linear System
Ruigang Wang, Nicholas H. Barbara, Max Revay +1
This paper proposes a nonlinear policy architecture for control of partially-observed linear dynamical systems providing built-in closed-loop stability guarantees. The policy is ba…
Multi-Stage Sparse Resource Allocation for Control of Spreading Processes over Networks
Vera L. J. Somers, Ian R. Manchester
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 an…
Contraction-Based Methods for Stable Identification and Robust Machine Learning: a Tutorial
Ian R. Manchester, Max Revay, Ruigang Wang
This tutorial paper provides an introduction to recently developed tools for machine learning, especially learning dynamical systems (system identification), with stability and rob…
Distributed Identification of Contracting and/or Monotone Network Dynamics
Max Revay, Jack Umenberger, Ian R. Manchester
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…
Minimizing the Risk of Spreading Processes via Surveillance Schedules and Sparse Control
Vera L. J. Somers, Ian R. Manchester
In this paper, we propose an optimization framework that combines surveillance schedules and sparse control to bound the risk of spreading processes such as epidemics and wildfires…
Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics
Ruigang Wang, Patrick J. W. Koelwijn, Ian R. Manchester +1
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 gy…
On the equivalence of contraction and Koopman approaches for nonlinear stability and control
Bowen Yi, Ian R. Manchester
In this paper we prove new connections between two frameworks for analysis and control of nonlinear systems: the Koopman operator framework and contraction analysis. Each method, i…
Preserving Privacy of the Influence Structure in Friedkin-Johnsen Systems
Jack Liell-Cock, Ian R. Manchester, Guodong Shi
The nature of information sharing in common distributed consensus algorithms permits network eavesdroppers to expose sensitive system information. An important parameter within dis…