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Learning-based Adaptive Control using Contraction Theory

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 نشر من قبل Hiroyasu Tsukamoto
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
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We present a deep learning-based adaptive control framework for nonlinear systems with multiplicatively separable parametrization, called aNCM - for adaptive Neural Contraction Metric. The framework utilizes a deep neural network to approximate a stabilizing adaptive control law parameterized by an optimal contraction metric. The use of deep networks permits real-time implementation of the control law and broad applicability to a variety of systems, including systems modeled with basis function approximation methods. We show using contraction theory that aNCM ensures exponential boundedness of the distance between the target and controlled trajectories even under the presence of the parametric uncertainty, robustly to the learning errors caused by aNCM approximation as well as external additive disturbances. Its superiority to the existing robust and adaptive control methods is demonstrated in a simple cart-pole balancing task.



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