Open AccessSelf‐tuning predictive control applicable to ship magnetic levitation damping device
Author(s) -
Zhang Hui,
Yan Jinghao,
Wang Weiran,
Xu Meng,
Ma Wenjing
Publication year - 2022
Publication title -
iet collaborative intelligent manufacturing
Language(s) - English
Resource type - Journals
ISSN - 2516-8398
DOI - 10.1049/cim2.12044
Subject(s) - control theory (sociology) , model predictive control , levitation , magnetic levitation , controller (irrigation) , recursive least squares filter , vibration , position (finance) , self tuning , computer science , engineering , control engineering , control (management) , pid controller , adaptive filter , algorithm , temperature control , mechanical engineering , agronomy , physics , finance , quantum mechanics , artificial intelligence , biology , economics , magnet
In the ship design, there are strict vibration‐proof requirements for precision instruments. Therefore, a ship repulsive magnetic levitation damping device is designed to achieve vibration reduction. And one self‐tuning predictive control method is proposed to achieve the stable levitation of this device. Firstly, a predictive control (MPC) method with state constraints and input constraints is adopted to realise the stable suspension of the floater. The MPC can solve the problem of position imbalance of the magnetic levitation system under the external complex disturbances. Secondly, a self‐tuning MPC method based on recursive least square is proposed to solve the problem caused by the fixed parameters of the traditional predictive controller. At the beginning of each control cycle, the recursive least‐squares (RLS) method is used to estimate the parameters of the system. Thus, the optimal control model could be obtained for the current situation. Then, this model is applied to the predictive controller to solve the problem of parameter fixation in the traditional predictive control. Finally, the simulation results show that it can improve the accuracy, dynamic response and anti‐interference performance obviously.