Home // International Journal On Advances in Intelligent Systems, volume 5, numbers 3 and 4, 2012 // View article

A Metaheuristic Particle Swarm Optimization Approach to Nonlinear Model Predictive Control

Authors:
Julian Mercieca
Simon G. Fabri

Keywords: particle swarm optimization; model predictive control; optimal control; nonlinear control; computational intelligence; swarm intelligence; evolutionary intelligence; artificial intelligence; metaheuristic algorithms

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 analysed. 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 Optimization. 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.

Pages: 357 to 369

Copyright: Copyright (c) to authors, 2012. Used with permission.

Publication date: December 31, 2012

Published in: journal

ISSN: 1942-2679