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An improved two‐loop model predictive control design for nonlinear robust reference tracking with practical advantages
Author(s) -
FarajzadehD. MohammadG.,
Sani S. K. Hosseini
Publication year - 2020
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2691
Subject(s) - control theory (sociology) , model predictive control , nonlinear system , computer science , constraint (computer aided design) , bounded function , controller (irrigation) , stability (learning theory) , tracking error , robust control , exponential stability , loop (graph theory) , control engineering , mathematical optimization , control (management) , engineering , mathematics , artificial intelligence , mechanical engineering , mathematical analysis , agronomy , physics , quantum mechanics , machine learning , combinatorics , biology
Summary In this paper, the nonlinear robust reference tracking problem is considered and an Improved Two‐Loop Nonlinear Model Predictive Control (ITL‐NMPC) design is proposed for a pre‐controlled system with bounded uncertainties subject to input and state constraints. In the industry, this scheme leads the lower design cost and fewer implementation risks since (i) it allows the existing controller to remain unchanged without any manipulation, (ii) it does not need the open‐loop model of the pre‐controlled system, and (iii) it does not require the terminal cost and constraint definition. For the proposed ITL‐NMPC, a practical parameter tuning algorithm is introduced and its robust exponential stability, as well as recursive feasibility, are guaranteed. Simulation results are used to better illustrate the effectiveness of the proposed approach and show that the proposed method not only improves the control performance of the pre‐controlled system but also reduces the design cost while satisfying the constraints.