Neural Network Adaptive Control with Long Short-Term Memory

TL;DR

This paper introduces a novel adaptive control architecture combining neural networks and LSTM to significantly enhance transient response and uncertainty compensation in control systems.

Contribution

It presents a new adaptive control method integrating LSTM with neural networks, improving transient response and uncertainty handling during sudden plant dynamics changes.

Findings

Enhanced transient response performance demonstrated in simulations

LSTM network effectively compensates for rapid changes in system dynamics

Stable control system verified through Lyapunov analysis

Abstract

In this study, we propose a novel adaptive control architecture, which provides dramatically better transient response performance compared to conventional adaptive control methods. What makes this architecture unique is the synergistic employment of a traditional, Adaptive Neural Network (ANN) controller and a Long Short-Term Memory (LSTM) network. LSTM structures, unlike the standard feed-forward neural networks, can take advantage of the dependencies in an input sequence, which can contain critical information that can help predict uncertainty. Through a novel training method we introduced, the LSTM network learns to compensate for the deficiencies of the ANN controller during sudden changes in plant dynamics. This substantially improves the transient response of the system and allows the controller to quickly react to unexpected events. Through careful simulation studies, we demonstrate that this architecture can improve the estimation accuracy on a diverse set of uncertainties for an indefinite time span. We also provide an analysis of the contributions of the ANN controller and LSTM network to the control input, identifying their individual roles in compensating low and high-frequency error dynamics. This analysis provides insight into why and how the LSTM augmentation improves the system's transient response. The stability of the overall system is also shown via a rigorous Lyapunov analysis.