5 papers
A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
Anand Gokhale, Anton V. Proskurnikov, Yu Kawano +1
This paper establishes a nonlinear separation principle based on contraction theory and derives sharp stability conditions for recurrent neural networks (RNNs). First, we introduce…
Output Corridor Impulsive Control of First-order Continuous System with Non-local Attractivity Analysis
Alexander Medvedev, Anton V. Proskurnikov
This paper addresses the design of an impulsive controller for a continuous scalar time-invariant linear plant that constitutes the simplest conceivable model of chemical kinetics.…
Contracting Neural Networks: Sharp LMI Conditions with Applications to Integral Control and Deep Learning
Anand Gokhale, Anton V. Proskurnikov, Yu Kawano +1
This paper studies contractivity of firing-rate and Hopfield recurrent neural networks. We derive sharp LMI conditions on the synaptic matrices that characterize contractivity of b…
Assessing Linear Control Strategies for Zero-Speed Fin Roll Damping
Nikita Savin, Elena Ambrosovskaya, Dmitry Romaev +1
Roll stabilization is a critical aspect of ship motion control, particularly for vessels operating in low-speed or zero-speed conditions, where traditional hydrodynamic fins lose t…
Nonlinear dynamics in pulse-modulated feedback drug dosing
Alexander Medvedev, Anton V. Proskurnikov, Zhanybai T. Zhusubaliyev
Pulse-modulated feedback is utilized in drug dosing to mimic sustained over a longer period of time manual discrete dose administration, the latter is in contrast with continuous d…