Data-Driven Stable Neural Feedback Loop Design

2024/05/03 by Zuxun Xiong, Han Wang, Xiong, Zuxun +5 · 1 citation
Computer Science · Engineering · #Control Systems and Identification #FOS: Mathematics #Fault Detection and Control Systems #Neural Networks and Applications #Optimization and Control (math.OC)

paper · pdf · doi:10.48550/arxiv.2405.02100

openalex publication_date 2024/05/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper proposes a data-driven approach to design a feedforward Neural Network (NN) controller with a stability guarantee for plants with unknown dynamics. We first introduce data-driven representations of stability conditions for Neural Feedback Loops (NFLs) with linear plants, which can be formulated into a semidefinite program (SDP). Subsequently, this SDP constraint is integrated into the NN training process to ensure stability of the feedback loop. The whole NN controller design problem can be solved by an iterative algorithm. Finally, we illustrate the effectiveness of the proposed method compared to model-based methods via numerical examples.

Cited by

• Data-Enabled Predictive Control for Nonlinear Systems Based on a Koopman Bilinear Realization

Related

• Stability Margins of Neural Network Controllers
• A Guaranteed-Stable Neural Network Approach for Optimal Control of Nonlinear Systems
• Stability Analysis using Quadratic Constraints for Systems with Neural Network Controllers
• Synthesizing Neural Network Controllers with Closed-Loop Dissipativity Guarantees
• React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-REN