Iterative dynamic linearization and identification of a nonlinear learning controller: A data-driven approach |
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| Institution: | 1. Institute of Artificial Intelligence and Control, School of Automation & Electronics Engineering, Qingdao University of Science & Technology, Qingdao 266061, PR China;2. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2G6, Canada;3. Advanced Control Systems Lab, School of Electronics & Information Engineering, Beijing Jiaotong University, Beijing 100044, PR China;1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;2. Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, China;3. Department of Automation, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China;4. School of Mechanical Engineering and Electronic Information China University of Geosciences, Wuhan 430074, China;5. Risk Control Department, China Unionpay Co., Ltd., Shanghai, China;1. Department of Inertia, Changcheng Institute of Metrology & Measurement, Beijing 100095, China;2. School of Automation, Beijing Institute of Technology, Beijing 100081, China;3. College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China |
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| Abstract: | 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. |
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