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Journal of Applied Mathematics
Volume 2014, Article ID 307809, 9 pages
Research Article

A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems

1Advanced Control Systems Lab, School of Electronic & Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2School of Automation & Electronic Engineering, Qingdao University of Science & Technology, Qingdao 266042, China

Received 15 May 2014; Accepted 23 July 2014; Published 12 August 2014

Academic Editor: Claudio H. Morales

Copyright © 2014 Shangtai Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A data-driven predictive terminal iterative learning control (DDPTILC) approach is proposed for discrete-time nonlinear systems with terminal tracking tasks, where only the terminal output tracking error instead of entire output trajectory tracking error is available. The proposed DDPTILC scheme consists of an iterative learning control law, an iterative parameter estimation law, and an iterative parameter prediction law. If the partial derivative of the controlled system with respect to control input is bounded, then the proposed control approach guarantees the terminal tracking error convergence. Furthermore, the control performance is improved by using more information of predictive terminal outputs, which are predicted along the iteration axis and used to update the control law and estimation law. Rigorous analysis shows the monotonic convergence and bounded input and bounded output (BIBO) stability of the DDPTILC. In addition, extensive simulations are provided to show the applicability and effectiveness of the proposed approach.