Despite the numerous advances in control theory over the past decades, humans' versatility in controlling complex systems is still irreplaceable due to their adaptive capabilities. Yet, when it comes to implementing adaptive controllers in piloted applications, unfavorable interactions of human pilots with control systems are observed in certain applications. While several studies exist in the literature that investigate pilot-controller interactions, they are primarily based on linear and fixed dynamics. These studies are useful to study the ideal system behavior, however, they may not be helpful in analyzing uncertainties and failures in system dynamics and the adaptive response of the human operator to these undesired occurrences. In this paper, we fill this gap by proposing a closed-loop system analysis consisting of adaptive dynamics for both the pilot and the flight control system. This analysis can offer guidance in designing adaptive control architectures to enhance safety measures in real-world manned applications.

No 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 highfrequency 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.

No abstract

No abstract