Open AccessIncremental state model in predictive control: A new fuzzy control proposal for nonlinear systems
Author(s) -
AlHadithi Basil Mohammed,
Adánez José Miguel,
Comina Mayra,
Jiménez Agustín
Publication year - 2022
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/cth2.12247
Subject(s) - control theory (sociology) , model predictive control , kalman filter , state vector , nonlinear system , fuzzy control system , multivariable calculus , computer science , state space representation , fuzzy logic , state space , mathematics , algorithm , control (management) , control engineering , artificial intelligence , engineering , statistics , physics , classical mechanics , quantum mechanics
In this work, a fuzzy predictive optimal control for multivariable nonlinear systems with pure time delays is presented. Therefore, dynamic local linear state models are used at each point of the state space obtained by fuzzy Takagi‐Sugeno (T‐S) modelling. The modelling error is considered as white noise, and the state is observed using the Kalman Filter (KF). This method does not take into account possible input constraints. Therefore, it applies to systems where there are no saturation problems. In this work, a new approach in the Model Predictive Control (MPC) method is proposed by calculating the control signal increment as a function of the error between a reference state vector and the prediction at N‐steps of the state vector instead of using the traditional MPC approach which is based on calculating the error between a reference and the predicted output. The proposed method has a satisfactory tracking performance. The main feature of the proposed method is that it attains computational savings compared to other methods that have used incremental state models, which making it more appropriate for real‐time applications.