5 papers · 1 filter
Koopman-based NMPC for Virtually Coupled Train Control System
Yiwen Zhang, Lorenzo Calogero, Shukai Li +2
This paper investigates an analytical Koopman-based nonlinear model predictive control (K-NMPC) approach for tracking control of virtually coupled train systems. A nonlinear train…
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…
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…
Solvability of the Output Corridor Control Problem by Pulse-Modulated Feedback
Alexander Medvedev, Anton V. Proskurnikov
The problem of maintaining the output of a positive time-invariant single-input single-output system within a predefined corridor of values is treated. For third-order plants posse…
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…