Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/29159
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

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