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

Shangtai Jin | Zendy; Zhongsheng Hou | Zendy; Ronghu Chi | Zendy

Open Access

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

Author(s) -

Shangtai Jin,

Zhongsheng Hou,

Ronghu Chi

Publication year - 2014

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2014/307809

Subject(s) - iterative learning control , bounded function , tracking error , control theory (sociology) , convergence (economics) , bibo stability , computer science , nonlinear system , terminal (telecommunication) , monotonic function , iterative method , trajectory , model predictive control , discrete time and continuous time , stability (learning theory) , tracking (education) , algorithm , mathematics , mathematical optimization , control (management) , artificial intelligence , machine learning , psychology , mathematical analysis , telecommunications , pedagogy , physics , quantum mechanics , astronomy , economics , economic growth , statistics

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

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