A Data-Driven Iterative Learning Control Framework Based on Controller Dynamic Linearization

A novel data-driven iterative learning control (ILC) framework is proposed in this paper for a class of unknown nonlinear repetitive discrete-time systems by applying the iterative dynamic linearization (IDL) technique, which is an extension of dynamic linearization (DL) in iteration domain. The prototype DL is applied for unknown nonlinear systems and then extended for unknown ideal nonlinear controllers. By applying the idea of the IDL for unknown general iterative learning controller, a novel learning controller with a learning gain in iteration domain is constructed, and the learning gain is automatically tuned with only requirement of the measured I/O data of the controlled nonlinear repetitive systems. The main contribution of this work is that most of the existing ILC controllers can be considered as a special case of this method. The convergence of the proposed data-driven ILC framework is guaranteed through rigorous theoretical analysis and the effectiveness is validated with simulation results.

[1]  Miroslav Krstic,et al.  Iterative learning control based on extremum seeking , 2016, Autom..

[2]  Danwei Wang,et al.  Data-driven optimal terminal iterative learning control with initial value dynamic compensation , 2016 .

[3]  Genzhong Wu,et al.  Adaptive Iterative Learning Control for Robot Manipulators , 2012 .

[4]  Han-Xiong Li,et al.  Feedback-Linearization-Based Neural Adaptive Control for Unknown Nonaffine Nonlinear Discrete-Time Systems , 2008, IEEE Transactions on Neural Networks.

[5]  Xuhui Bu,et al.  Robust model free adaptive control with measurement disturbance , 2012 .

[6]  Riccardo Marino,et al.  Adaptive Learning Control of Nonlinear Systems by Output Error Feedback , 2007, IEEE Transactions on Automatic Control.

[7]  E. Rogers,et al.  Iterative learning control for discrete-time systems with exponential rate of convergence , 1996 .

[8]  Danwei Wang,et al.  Neural network based terminal iterative learning control for uncertain nonlinear non‐affine systems , 2015 .

[9]  Kumpati S. Narendra,et al.  Identification and control of a nonlinear discrete-time system based on its linearization: a unified framework , 2004, IEEE Transactions on Neural Networks.

[10]  Zhongsheng Hou,et al.  Model Free Adaptive Control: Theory and Applications , 2013 .

[11]  Zhongsheng Hou,et al.  Local learning-based model-free adaptive predictive control for adjustment of oxygen concentration in syngas manufacturing industry , 2016 .

[12]  Huijun Gao,et al.  An Overview of Dynamic-Linearization-Based Data-Driven Control and Applications , 2017, IEEE Transactions on Industrial Electronics.

[13]  Shangtai Jin,et al.  Full form dynamic linearization controller based data-driven model free adaptive control , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[14]  Yugeng Xi,et al.  NONLINEAR MULTI-MODEL PREDICTIVE CONTROL , 1996 .

[15]  Tae-Yong Kuc,et al.  An adaptive learning controller for MIMO uncertain feedback linearizable nonlinear systems , 2015 .

[16]  Chiang-Ju Chien,et al.  A Combined Adaptive Law for Fuzzy Iterative Learning Control of Nonlinear Systems With Varying Control Tasks , 2008, IEEE Transactions on Fuzzy Systems.

[17]  Peng Shi,et al.  A Novel Model-Free Adaptive Control Design for Multivariable Industrial Processes , 2014, IEEE Transactions on Industrial Electronics.

[18]  Masaru Uchiyama,et al.  Formation of High-Speed Motion Pattern of a Mechanical Arm by Trial , 1978 .

[19]  Rui Yan,et al.  Adaptive Learning Control for Finite Interval Tracking Based on Constructive Function Approximation and Wavelet , 2011, IEEE Transactions on Neural Networks.

[20]  Patrizio Tomei,et al.  Adaptive Learning Control of Nonlinear Systems by Output Error Feedback , 2004, IEEE Transactions on Automatic Control.

[21]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[22]  Jian-Xin Xu,et al.  A survey on iterative learning control for nonlinear systems , 2011, Int. J. Control.

[23]  Peng Shi,et al.  Adaptive Observer Based Data-Driven Control for Nonlinear Discrete-Time Processes , 2014, IEEE Transactions on Automation Science and Engineering.