24 citations · 71 across the 30 of their papers we have counts for
5 papers · 1 filter
Reduced-Order Nonlinear Observers via Contraction Analysis and Convex Optimization
Bowen Yi, Ruigang Wang, Ian R. Manchester
In this paper, we propose a new approach to design globally convergent reduced-order observers for nonlinear control systems via contraction analysis and convex optimization. Despi…
Lipschitz Bounded Equilibrium Networks
Max Revay, Ruigang Wang, Ian R. Manchester
This paper introduces new parameterizations of equilibrium neural networks, i.e. networks defined by implicit equations. This model class includes standard multilayer and residual…
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…
A Convex Parameterization of Robust Recurrent Neural Networks
Max Revay, Ruigang Wang, Ian R. Manchester
Recurrent neural networks (RNNs) are a class of nonlinear dynamical systems often used to model sequence-to-sequence maps. RNNs have excellent expressive power but lack the stabili…
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…