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Title: | A metaheuristic particle swarm optimization approach to nonlinear model predictive control |
Authors: | Mercieca, Julian Fabri, Simon G. |
Keywords: | Predictive control Nonlinear control theory Computational intelligence Swarm intelligence Artificial intelligence |
Issue Date: | 2012 |
Publisher: | John Wiley and Sons Ltd. |
Citation: | Mercieca, J., & Fabri, S. G. (2012). A metaheuristic particle swarm optimization approach to nonlinear model predictive control. International Journal On Advances in Intelligent Systems, 5(3), 357-369. |
Abstract: | This paper commences with a short review on optimal control for nonlinear systems, emphasizing the Model Predictive approach for this purpose. It then describes the Particle Swarm Optimization algorithm and how it could be applied to nonlinear Model Predictive Control. On the basis of these principles, two novel control approaches are proposed and anal- ysed. One is based on optimization of a numerically linearized perturbation model, whilst the other avoids the linearization step altogether. The controllers are evaluated by simulation of an inverted pendulum on a cart system. The results are compared with a numerical linearization technique exploiting conventional convex optimization methods instead of Particle Swarm Opti- mization. In both approaches, the proposed Swarm Optimization controllers exhibit superior performance. The methodology is then extended to input constrained nonlinear systems, offering a promising new paradigm for nonlinear optimal control design. |
URI: | https://www.um.edu.mt/library/oar//handle/123456789/29159 |
ISSN: | 15420973 15420981 |
Appears in Collections: | Scholarly Works - FacEngSCE |
Files in This Item:
File | Description | Size | Format | |
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A_metaheuristic_particle_swarm_optimization_approach_to_nonlinear_model_predictive_control.pdf | 917.46 kB | Adobe PDF | View/Open |
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