Dynamic Obstacles Avoidance Using Nonlinear Model Predictive Control
| Autor: | Mukhtar Sani, Bogdan Robu, Ahmad Hably |
|---|---|
| Přispěvatelé: | GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), GIPSA - Modelling and Optimal Decision for Uncertain Systems (GIPSA-MODUS), GIPSA Pôle Automatique et Diagnostic (GIPSA-PAD) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Obstacles Avoidance
Computer science Computation [SPI.NRJ]Engineering Sciences [physics]/Electric power Stability (learning theory) Mobile robot Function (mathematics) Optimal control NMPC Nonlinear programming [SPI.AUTO]Engineering Sciences [physics]/Automatic Mobile Robots Model predictive control Control theory |
| Zdroj: | IECON 2021-The 47th Annual Conference of the IEEE Industrial Electronics Society IECON 2021-The 47th Annual Conference of the IEEE Industrial Electronics Society, Oct 2021, Toronto (virtual), Canada. ⟨10.1109/IECON48115.2021.9589658⟩ IECON |
| DOI: | 10.1109/IECON48115.2021.9589658⟩ |
| Popis: | International audience; In this paper, a Nonlinear Model Predictive Control (NMPC) has been employed to solve point-stabilization problems with static and dynamic obstacles avoidance. The algorithm was implemented on a mobile robot with two differential drive wheels. In NMPC, a cost function is formulated to minimize an error between the reference and the current state of the system subject to constraints. The major drawback of NMPC is the computation time, which results from predicting the system's state over a horizon. However, in this work, the resulting optimal control problem is converted to a discrete nonlinear programming problem using a recently developed toolkit. Dynamic obstacles avoidance is incorporated as a time-varying constraint and can be affected by a short prediction horizon. On the other hand, a long prediction horizon affects the computation time. For this, a terminal state penalty is added to the cost function to guarantee the stability of the control using a relatively shorter prediction horizon. The performance of the proposed controller achieving both static and dynamic obstacles avoidance is verified using several simulation scenarios. |
| Databáze: | OpenAIRE |
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