کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
750373 895081 2009 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Learning from neural control of nonlinear systems in normal form
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
Learning from neural control of nonlinear systems in normal form
چکیده انگلیسی

A deterministic learning theory was recently proposed which states that an appropriately designed adaptive neural controller can learn the system internal dynamics while attempting to control a class of simple nonlinear systems. In this paper, we investigate deterministic learning from adaptive neural control (ANC) of a class of nonlinear systems in normal form with unknown affine terms. The existence of the unknown affine terms makes it difficult to achieve learning by using previous methods. To overcome the difficulties, firstly, an extension of a recent result is presented on stability analysis of linear time-varying (LTV) systems. Then, with a state transformation, the closed-loop control system is transformed into a LTV form for which exponential stability can be guaranteed when a partial persistent excitation (PE) condition is satisfied. Accurate approximation of the closed-loop control system dynamics is achieved in a local region along a recurrent orbit of closed-loop signals. Consequently, learning of control system dynamics (i.e. closed-loop identification) from adaptive neural control of nonlinear systems with unknown affine terms is implemented.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Systems & Control Letters - Volume 58, Issue 9, September 2009, Pages 633–638
نویسندگان
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