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- W3119579663 abstract "In this paper, we investigate a novel control architecture and algorithm for incorporating preadaption functions. We propose a preadaptation mechanism that can augment any adaptive control scheme and improve its resilience. We also propose a preadaptation learner that learns the preadaption function with experience, which removes the complexity of designing and fine tuning the preadaptation function specific to the system to be controlled. Through simulations of a flight control system we illustrate the effectiveness of the preadaptation mechanism in improving the adaptation. We show that the preadaptation mechanism we propose can reduce the peak of the response by as much as $50%$. The scenarios we present also show that the preadaptation mechanism is effective across a wide range of scenarios suggesting that the mechanism is reliable." @default.
- W3119579663 created "2021-01-18" @default.
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- W3119579663 date "2021-01-04" @default.
- W3119579663 modified "2023-10-16" @default.
- W3119579663 title "Cognitive Preadaptation for Resilient Adaptive Control" @default.
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- W3119579663 doi "https://doi.org/10.2514/6.2021-0786" @default.
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