Open AccessAn Adaptive Predictive Control Method Based on State-space Model
Author(s) -
Xiaosuo Luo
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1750/1/012031
Subject(s) - model predictive control , control theory (sociology) , state space , controller (irrigation) , constraint (computer aided design) , nonlinear system , continuous stirred tank reactor , state space representation , adaptive control , computer science , state (computer science) , identification (biology) , process (computing) , quadratic programming , control engineering , mathematical optimization , control (management) , mathematics , engineering , algorithm , artificial intelligence , statistics , physics , geometry , botany , quantum mechanics , chemical engineering , agronomy , biology , operating system
An adaptive state-space model predictive control strategy is proposed for complex industrial processes with nonlinear, time-varying and constrained characteristics. The state-space model obtained by on-line identification algorithm is used as the system model, and the indirect form is used to design the adaptive predictive controller. The controller includes quadratic programming solution to the constraint problem. The effectiveness of the proposed control strategy is verified by the simulation experiment of 2-CSTR process control.