Neural Network Adaptive Control With Long Short-Term Memory

Abstract

In this study, we propose a novel adaptive control architecture that provides dramatically better transient response performance compared to conventional adaptive control methods. This is accomplished by the synergistic employment of a traditional adaptive neural network (ANN) controller and a long short-term memory (LSTM) network. LSTM structures can take advantage of the dependencies in an input sequence, which can help predict uncertainty. We introduce a training approach through which the LSTM network learns to compensate for the deficiencies of the ANN controller. This 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 improves the estimation accuracy on a diverse set of uncertainties. We also provide an analysis of the contributions of the ANN controller and the LSTM network, identifying their 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 analyzed via a rigorous Lyapunov analysis.