Offset-free setpoint tracking using neural network controllers

• URL: http://arxiv.org/abs/2011.14006v2
• Date: Thu, 29 Apr 2021 17:10:14 GMT
• Title: Offset-free setpoint tracking using neural network controllers
• Authors: Patricia Pauli, Johannes K\"ohler, Julian Berberich, Anne Koch and Frank Allg\"ower
• Abstract summary: We present a method to analyze local and global stability in offset-free setpoint tracking using neural network controllers. We provide ellipsoidal inner approximations of the corresponding region of attraction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 approximations of the corresponding region of attraction. We consider a feedback interconnection of a linear plant in connection with a neural network controller and an integrator, which allows for offset-free tracking of a desired piecewise constant reference that enters the controller as an external input. Exploiting the fact that activation functions used in neural networks are slope-restricted, we derive linear matrix inequalities to verify stability using Lyapunov theory. After stating a global stability result, we present less conservative local stability conditions (i) for a given reference and (ii) for any reference from a certain set. The latter result even enables guaranteed tracking under setpoint changes using a reference governor which can lead to a significant increase of the region of attraction. Finally, we demonstrate the applicability of our analysis by verifying stability and offset-free tracking of a neural network controller that was trained to stabilize a linearized inverted pendulum.

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