Polynomial chaos explicit solution of the optimal control problem in model predictive control
| Autor: | F. De Belie, Tom Lefebvre, Guillaume Crevecoeur |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Polynomial Mathematical optimization polynomial chaos expansion Polynomial chaos Technology and Engineering Computation 020206 networking & telecommunications 02 engineering and technology Optimal control Nonlinear system Model predictive control nonlinear model based control 020901 industrial engineering & automation Real-time Control System 0202 electrical engineering electronic engineering information engineering optimal feedback real-time control Variable (mathematics) Mathematics |
| Zdroj: | 2017 IEEE INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM) AIM |
| ISSN: | 2159-6255 |
| Popis: | A difficulty still hindering the widespread application of Model Predictive Control (MPC) methodologies, remains the computational burden that is related to solving the associated Optimal Control (OC) problem for every control period. In contrast to numerous approximation techniques that pursue acceleration of the online optimization procedure, relatively few work has been devoted towards shifting the optimization effort to a precomputational phase, especially for nonlinear system dynamics. Recently, interest revived in the theory of general Polynomial Chaos (gPC) in order to appraise the influence of variable parameters on dynamic system behaviour and proved to yield reliable results. This article establishes an explicit solution of the multi-parametric Nonlinear Problem (mp-NLP) based on the theoretical framework of gPC, which enabled a polynomial approximated nonlinear feedback law formulation. This resulted in real-time computations allowing for real-time MPC, with corresponding control frequencies up to 2 kHz |
| Databáze: | OpenAIRE |
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