Neural Network Optimal Feedback Control with Guaranteed Local Stability

TL;DR

This paper introduces novel neural network architectures for feedback control that guarantee local stability in high-dimensional nonlinear systems, addressing the unpredictability of neural network controllers.

Contribution

The paper proposes new neural network designs that ensure local asymptotic stability while maintaining approximation capabilities for optimal feedback control.

Findings

Proposed architectures guarantee local stability in simulations.

Standard neural networks can fail to stabilize systems.

New architectures achieve near-optimal control performance.

Abstract

Recent research shows that supervised learning can be an effective tool for designing near-optimal feedback controllers for high-dimensional nonlinear dynamic systems. But the behavior of neural network controllers is still not well understood. In particular, some neural networks with high test accuracy can fail to even locally stabilize the dynamic system. To address this challenge we propose several novel neural network architectures, which we show guarantee local asymptotic stability while retaining the approximation capacity to learn the optimal feedback policy semi-globally. The proposed architectures are compared against standard neural network feedback controllers through numerical simulations of two high-dimensional nonlinear optimal control problems: stabilization of an unstable Burgers-type partial differential equation, and altitude and course tracking for an unmanned aerial vehicle. The simulations demonstrate that standard neural networks can fail to stabilize the dynamics even when trained well, while the proposed architectures are always at least locally stabilizing and can achieve near-optimal performance.