Robust feedback feed-forward PD-type iterative learning control for uncertain discrete systems over finite frequency ranges

Author:

Zou Wei1ORCID,Shen Yanxia1,Paszke Wojciech2,Tao Hongfeng1

Affiliation:

1. Engineering Research Center of Internet of Things Technology Applications, Ministry of Education, Jiangnan University, China

2. Institute of Automation, Electronic and Electrical Engineering, University of Zielona Góra, Poland

Abstract

This paper proposes a robust feedback feed-forward proportional-derivative type (PD-type) iterative learning control (ILC) scheme for a class of linear discrete systems with polytopic uncertainty over finite frequency ranges. First, the ILC process is transformed into an equivalent discrete linear repetitive process. Then, with the help of the generalized Kalman–Yakubovich–Popov lemma, the issue of a robust control law design algorithm in the process model is converted into the problem of solutions to the corresponding linear matrix inequality conditions. This procedure not only meets the robust performance specifications along the trial, but also guarantees the monotonic converge of the trial-to-trial error dynamics over finite frequency ranges. Finally, the simulations for a direct current servo motor system are adopted to show the effectiveness and superiority of the proposed method. Compared with previously established works, the new algorithm is more effective in delivering higher performance and achieving better tracking effect.

Funder

National Natural Science Foundation of China

the Fundamental Research Funds for the Central Universities

National Science Centre in Poland

Postgraduate Research & Practice Innovation Program of Jiangsu Province

Publisher

SAGE Publications

Subject

Instrumentation

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