Feedback-Assisted Iterative Learning Model Predictive Control with Nonlinear Fuzzy Model | Zendy

Xiangjie Liu | Zendy; Ke Xi | Zendy

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Feedback-Assisted Iterative Learning Model Predictive Control with Nonlinear Fuzzy Model

Author(s) -

Xiangjie Liu,

Ke Xi

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/874705

Subject(s) - iterative learning control , feed forward , control theory (sociology) , model predictive control , control engineering , computer science , fuzzy logic , nonlinear system , fuzzy control system , engineering , control (management) , artificial intelligence , physics , quantum mechanics

Iterative learning control (ILC), due to its advantage of requiring less system knowledge, can serve as a feedforward signal in system control. ILC can be combined with model predictive control (MPC) to constitute a feedforward-feedback configuration. In this scheme, ILC provides most of the control signal and copes with the repetitive disturbances. MPC provides the supplementary control for regulation purpose and also for nonrepeating disturbance rejection. Considering the nonlinear industrial process, this paper establishes the plant nonlinear fuzzy model to constitute the fuzzy model-based feedback-assisted ILC. The integrated control strategy can achieve wide-range operation and good tracking performance. The performance of the feedback-assisted ILC is illustrated by a steam-boiler system

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