Nonlinear model predictive control (NMPC) and real-time dynamic optimization (RTDO) both based on a receding horizon methodology are fast spreading outside their common application field towards some scientific and industrial areas particularly far from their original cradle. Unfortunately, algorithms and numerical methods behind NMPC and RTDO are not a prerogative of these novel application fields with the result of having not so performing or furthermore ineffective solutions. From this perspective, the possibility to provide a generalized approach to solve NMPC and RTDO problems able to automatically select the best combination of algorithms could have the twofold aim of supporting an inexpert user in implementing these appealing methodologies and to favor their spreading in other scientific areas.

A Comprehensive and General Class for Solving Nonlinear Model Predictive Control and Dynamic Optimization

MANENTI, FLAVIO;
2010-01-01

Abstract

Nonlinear model predictive control (NMPC) and real-time dynamic optimization (RTDO) both based on a receding horizon methodology are fast spreading outside their common application field towards some scientific and industrial areas particularly far from their original cradle. Unfortunately, algorithms and numerical methods behind NMPC and RTDO are not a prerogative of these novel application fields with the result of having not so performing or furthermore ineffective solutions. From this perspective, the possibility to provide a generalized approach to solve NMPC and RTDO problems able to automatically select the best combination of algorithms could have the twofold aim of supporting an inexpert user in implementing these appealing methodologies and to favor their spreading in other scientific areas.
2010
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/579678
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