24 citations · 71 across the 30 of their papers we have counts for
16 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…
Remarks on Lipschitz-Minimal Interpolation: Generalization Bounds and Neural Network Implementation
Arthur C. B. de Oliveira, Ruigang Wang, Ian R. Manchester +1
This note establishes a theoretical framework for finding (potentially overparameterized) approximations of a function on a compact set with a-priori bounds for the generalization…
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…
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…
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…
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…
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…
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 necessary conditions of tracking control for nonlinear systems via contraction analysis
Bowen Yi, Ruigang Wang, Ian R. Manchester
In this paper we address the problem of tracking control of nonlinear systems via contraction analysis. The necessary conditions of the systems which can achieve universal asymptot…
Virtual Control Contraction Metrics: Convex Nonlinear Feedback Design via Behavioral Embedding
Ruigang Wang, Roland Tóth, Patrick J. W. Koelwijn +1
This paper presents a systematic approach to nonlinear state-feedback control design that has three main advantages: (i) it ensures exponential stability and -gain…
Continuous-time Dynamic Realization for Nonlinear Stabilization via Control Contraction Metrics
Ruigang Wang, Ian R. Manchester
Nonlinear stabilization using control contraction metric (CCM) method usually involves an online optimization problem to compute a minimal geodesic (a shortest path) between pair o…
A Comparison of LPV Gain Scheduling and Control Contraction Metrics for Nonlinear Control
Ruigang Wang, Roland Tóth, Ian R. Manchester
Gain-scheduled control based on linear parameter-varying (LPV) models derived from local linearizations is a widespread nonlinear technique for tracking time-varying setpoints. Rec…
Robust Contraction Analysis of Nonlinear Systems via Differential IQC
Ruigang Wang, Ian R. Manchester
We present a new approach to verifying contraction and -gain of uncertain nonlinear systems, extending the well-known method of integral quadratic constraints. The uncertain s…