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

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

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 con...

Full description

Bibliographic Details
Main Authors: Shangtai Jin, Zhongsheng Hou, Ronghu Chi
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2014/307809
id doaj-6cfef9bb560e43b69e80014124352c76
record_format Article
spelling doaj-6cfef9bb560e43b69e80014124352c762020-11-24T20:58:45ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/307809307809A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear SystemsShangtai Jin0Zhongsheng Hou1Ronghu Chi2Advanced Control Systems Lab, School of Electronic & Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaAdvanced Control Systems Lab, School of Electronic & Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Automation & Electronic Engineering, Qingdao University of Science & Technology, Qingdao 266042, ChinaA 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.http://dx.doi.org/10.1155/2014/307809
collection DOAJ
language English
format Article
sources DOAJ
author Shangtai Jin
Zhongsheng Hou
Ronghu Chi
spellingShingle Shangtai Jin
Zhongsheng Hou
Ronghu Chi
A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems
Journal of Applied Mathematics
author_facet Shangtai Jin
Zhongsheng Hou
Ronghu Chi
author_sort Shangtai Jin
title A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems
title_short A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems
title_full A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems
title_fullStr A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems
title_full_unstemmed A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems
title_sort novel data-driven terminal iterative learning control with iteration prediction algorithm for a class of discrete-time nonlinear systems
publisher Hindawi Limited
series Journal of Applied Mathematics
issn 1110-757X
1687-0042
publishDate 2014-01-01
description 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.
url http://dx.doi.org/10.1155/2014/307809
work_keys_str_mv AT shangtaijin anoveldatadriventerminaliterativelearningcontrolwithiterationpredictionalgorithmforaclassofdiscretetimenonlinearsystems
AT zhongshenghou anoveldatadriventerminaliterativelearningcontrolwithiterationpredictionalgorithmforaclassofdiscretetimenonlinearsystems
AT ronghuchi anoveldatadriventerminaliterativelearningcontrolwithiterationpredictionalgorithmforaclassofdiscretetimenonlinearsystems
AT shangtaijin noveldatadriventerminaliterativelearningcontrolwithiterationpredictionalgorithmforaclassofdiscretetimenonlinearsystems
AT zhongshenghou noveldatadriventerminaliterativelearningcontrolwithiterationpredictionalgorithmforaclassofdiscretetimenonlinearsystems
AT ronghuchi noveldatadriventerminaliterativelearningcontrolwithiterationpredictionalgorithmforaclassofdiscretetimenonlinearsystems
_version_ 1716784707558965248

Similar Items