34 citations · 210 across the 68 of their papers we have counts for
9 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…
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…
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…
Initial-Value Privacy of Linear Dynamical Systems
Lei Wang, Ian R. Manchester, Jochen Trumpf +1
This paper studies initial-value privacy problems of linear dynamical systems. We consider a standard linear time-invariant system with random process and measurement noises. For s…
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…
Sparse Resource Allocation for Control of Spreading Processes via Convex Optimization
Vera L. J. Somers, Ian R. Manchester
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…
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…
Path following of a class of underactuated mechanical systems via immersion and invariance-based orbital stabilization
Bowen Yi, Romeo Ortega, Ian R. Manchester +1
This paper aims to provide a new problem formulation of path following for mechanical systems without time parameterization nor guidance laws, namely, we express the control object…