Browsing by Subject "Human-in-the-loop control"

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Open Access
An adaptive human pilot model for adaptively controlled systems
(IEEE, 2021-12-17) Habboush, Abdullah; Yıldız, Yıldıray
Despite their success in handling uncertain dynamical systems that are prone to failure, adaptive controllers are observed to have unfavorable interactions with human pilots in certain applications. To alleviate this problem, we need to evaluate the safety and performance of adaptive controllers in the simulation environment using realistic pilot models before conducting flight tests. While many useful human pilot models exist in the literature, models that are adequate for the prediction of adaptive human-adaptive controller interactions are yet to be available. In this letter, we fill this gap by proposing an adaptive human pilot model suited for the prediction of human behavior in the loop with an adaptive controller. The model can serve as a valuable tool guiding the design of adaptive controllers so as to ensure smooth pilot-controller interactions.
Open Access
Towards improved adaptive control: human pilot models & memory-augmented architectures
(2023-07) Habboush, Abdullah
To facilitate the implementation of adaptive control methods, this dissertation introduces novel solutions to key problems that hinder the employment of adaptive controllers in industrial applications. We present techniques that are inspired by humans’ versatility in the control loop, where we focus on understanding how humans adapt in the face of anomalies, and how they use their memory to better recover from them. Towards that end, we propose adaptive human pilot models suited for the prediction of human behavior in the loop with an adaptive controller. These models serve as valuable tools to test the interactions between human pilots and adaptive control systems in the simulation environment in order to ensure safe operation in the presence of an anomaly. Furthermore, the development of the models is carried out based on rigorous Lyapunov stability analyses, which can provide analytical insights into how to better design adaptive con-trollers for manned applications. Apart from their unfavorable interactions with human pilots, another issue that accounts for the scarce employment of adaptive controllers in piloted applications lies in their transient characteristics. While numerous works are devoted to improving the transients of adaptive control systems, in this dissertation, we focus on taking advantage of it ﬁrst by providing adaptive controllers with human-like memory capabilities. We propose a memory architecture that can make use of stored data about the transients of previously experienced anomalies to aid in obtaining a resilient system against uncertainties. Thus, the proposed memory architecture enables adaptive controllers to rely on memory rather than exploration to better recover from familiar anomalies. The eﬀectiveness of the architecture is validated through numerical simulations, and a rigorous Lyapunov stability analysis is provided.