2 citations · 5 across the 9 of their papers we have counts for
5 papers · 1 filter
State space representations of the Roesser type for convolutional layers
Patricia Pauli, Dennis Gramlich, Frank Allgöwer
From the perspective of control theory, convolutional layers (of neural networks) are 2-D (or N-D) linear time-invariant dynamical systems. The usual representation of convolutiona…
Learning Soft Constrained MPC Value Functions: Efficient MPC Design and Implementation providing Stability and Safety Guarantees
Nicolas Chatzikiriakos, Kim P. Wabersich, Felix Berkel +2
Model Predictive Control (MPC) can be applied to safety-critical control problems, providing closed-loop safety and performance guarantees. Implementation of MPC controllers requir…
Bounding the difference between model predictive control and neural networks
Ross Drummond, Stephen R. Duncan, Matthew C. Turner +2
There is a growing debate on whether the future of feedback control systems will be dominated by data-driven or model-driven approaches. Each of these two approaches has their own…
Linear systems with neural network nonlinearities: Improved stability analysis via acausal Zames-Falb multipliers
Patricia Pauli, Dennis Gramlich, Julian Berberich +1
In this paper, we analyze the stability of feedback interconnections of a linear time-invariant system with a neural network nonlinearity in discrete time. Our analysis is based on…
Offset-free setpoint tracking using neural network controllers
Patricia Pauli, Johannes Köhler, Julian Berberich +2
In this paper, we present a method to analyze local and global stability in offset-free setpoint tracking using neural network controllers and we provide ellipsoidal inner approxim…