13 papers
Localized Stabilization of Transport PDEs by Interior Flux Feedback
Constantinos Kitsos, Ian R. Manchester, Ian 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…
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 robust recurrent neural networks for machine learning and data-driven control. We const…
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…
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…
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…