nonlinear model predictive control (NMPC) can directly handle multi-input multi-output nonlinear systems and explicitly consider input and state constraints. However, the computational load for nonlinearprogramming (...
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nonlinear model predictive control (NMPC) can directly handle multi-input multi-output nonlinear systems and explicitly consider input and state constraints. However, the computational load for nonlinearprogramming (NLP) of large-scale systems limits the range of possible applications and degrades NMPC performance. An NLP sensitivity based approach, advanced-step NMPC, has been developed to address the online computational load. In addition, for cases where the NLP solving time exceeds one sampling time, two types of advanced-multi-step NMPC (amsNMPC), parallel and serial, have been proposed. However, in previous studies, a serial amsNMPC could not be applied to large-scale problems because of the size of extended Karush-Kuhn-Tucker matrix and its Schur complement decomposition, and the robustness was analyzed under a conservative assumption for memory effects. In this paper, we propose a serial amsNMPC using an extended sensitivity method to increase the online computation speed further. We successfully apply it to a large-scale air separation unit using the sparse matrix handling packages of Python, Pyomo, and k_aug tools. Furthermore, an auxiliary NLP formulation is defined to analyze the robustness. Using this with the key properties of an extended sensitivity matrix, we can prove robustness while avoiding the memory effects term. (C) 2020 Elsevier Ltd. All rights reserved.
In this work we present a novel multi-parametric nonlinearprogramming (mp-NLP) algorithm for explicit multi-parametric nonlinear model predictive control (mp-NMPC). The algorithm is based on (i) local sensitivity ana...
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In this work we present a novel multi-parametric nonlinearprogramming (mp-NLP) algorithm for explicit multi-parametric nonlinear model predictive control (mp-NMPC). The algorithm is based on (i) local sensitivity analysis of nonlinear programs (NLP), and (ii) an exploration procedure which makes use of successive linearizations of the dynamic system and the nonlinear constraints. The algorithm is illustrated with an example problem drawn from the open literature.
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