Stability-Preserving Online Adaptation of Neural Closed-loop Maps

TL;DR

This paper introduces a method for online updating neural network controllers in nonlinear systems that guarantees stability through gain-based conditions, enabling adaptive control without risking destabilization.

Contribution

It presents a stability-preserving update mechanism for neural controllers using gain-based conditions, allowing safe online adaptation in nonlinear control systems.

Findings

Guarantees closed-loop stability after online controller updates.

Demonstrates improved performance over static and naive online controllers.

Applicable to systems with time-varying objectives and disturbances.

Abstract

The growing complexity of modern control tasks calls for controllers that can react online as objectives and disturbances change, while preserving closed-loop stability. Recent approaches for improving the performance of nonlinear systems while preserving closed-loop stability rely on time-invariant recurrent neural-network controllers, but offer no principled way to update the controller during operation. Most importantly, switching from one stabilizing policy to another can itself destabilize the closed-loop. We address this problem by introducing a stability-preserving update mechanism for nonlinear, neural-network-based controllers. Each controller is modeled as a causal operator with bounded $\ell_p$-gain, and we derive gain-based conditions under which the controller may be updated online. These conditions yield two practical update schemes, time-scheduled and state-triggered, that guarantee the closed-loop remains $\ell_p$-stable after any number of updates. Our analysis further shows that stability is decoupled from controller optimality, allowing approximate or early-stopped controller synthesis. We demonstrate the approach on nonlinear systems with time-varying objectives and disturbances, and show consistent performance improvements over static and naive online baselines while guaranteeing stability.