Robust MPC for a non-linear system _ a neural network approach

European Workshop on Advanced Control and Diagnosis

Luzar, Marcel^1 ; Witczak, Marcin^1

Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, Zielona Góra

65-246, Poland^1

关键词: Actuator saturations;    Compensation problems;    Domain of attraction;    Fault tolerant control;    Linear parameter varying models;    Predictive control;    Robust controllers;    Robust invariant set;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/570/3/032002/pdf
DOI  :  10.1088/1742-6596/570/3/032002

来源: IOP

【 摘 要 】

The aim of the paper is to design a robust actuator fault-tolerant control for a non-linear discrete-time system. Considered system is described by the Linear Parameter-Varying (LPV) model obtained with recurrent neural network. The proposed solution starts with a discretetime quasi-LPV system identification using artificial neural network. Subsequently, the robust controller is proposed, which does not take into account actuator saturation level and deals with the previously estimated faults. To check if the compensation problem is feasible, the robust invariant set is employed, which takes into account actuator saturation level. When the current state does not belong to the set, then a predictive control is performed in order to make such set larger. This makes it possible to increase the domain of attraction, which makes the proposed methodology an efficient solution for the fault-tolerant control. The last part of the paper presents an experimental results regarding wind turbines.