1. Data-Driven Stable Neural Feedback Loop Design
- Author
-
Xiong, Zuxun, Wang, Han, Zhao, Liqun, and Papachristodoulou, Antonis
- Subjects
Mathematics - Optimization and Control - Abstract
This paper proposes a data-driven approach to design a feedforward Neural Network (NN) controller with a stability guarantee for systems with unknown dynamics. We first introduce data-driven representations of stability conditions for Neural Feedback Loops (NFLs) with linear plants. These conditions are then formulated into a semidefinite program (SDP). Subsequently, this SDP constraint is integrated into the NN training process resulting in a stable NN controller. We propose an iterative algorithm to solve this problem efficiently. Finally, we illustrate the effectiveness of the proposed method and its superiority compared to model-based methods via numerical examples.
- Published
- 2024