Premium
Nonlinear model predictive control with regulable computational cost
Author(s) -
He Y. Q.,
Han J. D.
Publication year - 2012
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.271
Subject(s) - pointwise , model predictive control , control theory (sociology) , nonlinear system , constraint (computer aided design) , computational complexity theory , stability (learning theory) , computer science , norm (philosophy) , controller (irrigation) , mathematical optimization , mathematics , control (management) , algorithm , artificial intelligence , machine learning , law , mathematical analysis , physics , geometry , quantum mechanics , agronomy , political science , biology
AbstractNonlinear model predictive control (NMPC) suffers from problems of closed loop instability and huge computational burden, which greatly limit its applications in real plants. In this paper, a new NMPC algorithm, whose stability is robust with respect to regulable computational cost, is presented. First, a new generalized pointwise min‐norm (GPMN) control, as well as its analytic form considering a super‐ball type input constraint, is given. Second, the GPMN controller is integrated into a normal NMPC algorithm as a structure of control input profile to be optimized, called GPMN enhanced NMPC (GPMN‐ENMPC). Finally, a numerical example is presented and simulation results exhibit the advantage of the GPMN‐ENMPC algorithm: computational cost can be regulated according to the computational resources with guaranteed stability.Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society