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Iterative dynamic linearization and identification of a nonlinear learning controller: A data-driven approach
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DOI：10.1016/j.jfranklin.2019.06.011.png)
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Abstract
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In this article, a nonlinear iterative learning controller (NILC) is developed using an iterative dynamic linearization (IDL) and a parameter iterative learning identification technique. First, the ideal NILC is transformed into a linear parameterized form by using a controller-oriented compact form IDL (controller-CFIDL) technique. Then an iterative learning identification approach is presented for tuning the parameters of the proposed controller using real-time I/O data. For the sake of analysis, a linear data model of the nonlinear plant is obtained by using the system-oriented IDL technology and a corresponding system parameter identification algorithm is developed in iteration domain. The convergence analysis is provided for the dynamically linearized nonlinear and nonaffine discrete-time system. The results are further extended by using a controller-oriented partial form iterative dynamic linearization (controller-PFIDL) method to gain a higher-order NILC utilizing additional control information from previous iterations. Simulations of two examples show the effectiveness of the proposed methods. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Keywords:
FREE ADAPTIVE-CONTROL
BATCH PROCESSES
SYSTEMS
DESIGN
ILC
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