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This paper studies learning from adaptive neural control (ANC) for a class of nonlinear strict-feedback systems with unknown affine terms. To achieve the purpose of learning, a simple input-to-state stability (ISS) modular ANC method is first presented to ensure the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in finite time. Subsequently, it is proven...
In this paper, we investigate deterministic learning from adaptive neural control of general Brunovsky systems, in which the affine terms are unknown functions of system states. We firstly present an extension of a recent result on stability analysis of linear time varying (LTV) systems. We then analyze the difficulties caused by the unknown affine term in deterministic learning for general Brunovsky...
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