Open AccessDistributed Nonlinear Model Predictive Control by Sequential Linearization and Accelerated Gradient Method
Author(s) -
Alexandra Grancharova,
Tor Arne Johansen,
Valeria Petrova
Publication year - 2016
Publication title -
ifac-papersonline
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 72
eISSN - 2405-8971
pISSN - 2405-8963
DOI - 10.1016/j.ifacol.2016.07.408
Subject(s) - linearization , model predictive control , nonlinear system , computer science , mathematical optimization , quadratic programming , dual (grammatical number) , control theory (sociology) , simple (philosophy) , computation , quadratic equation , nonlinear programming , nonlinear model , gradient method , convex optimization , regular polygon , mathematics , algorithm , control (management) , artificial intelligence , physics , quantum mechanics , art , philosophy , geometry , literature , epistemology
A suboptimal approach to distributed NMPC for nonlinear interconnected systems subject to constraints is proposed. The objective is to develop a computationally efficient approach. The suggested method is based on a sequential linearization of the nonlinear system dynamics and finding a suboptimal solution of the resulting Quadratic Programming problem by using distributed iterations of the dual accelerated gradient method. The benefits of the approach are reduced complexity of the on-line computations, and simple software implementation, which makes it appropriate for embedded distributed convex NMPC. The proposed method is illustrated with simulations on the model of a quadruple-tank system.
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