Integrating Particle Swarm Optimization with Analytical Nonlinear Model Predictive Control for Nonlinear Hybrid Systems

Jean Thomas

2015

Abstract

The computation load remains the main challenge facing the control techniques of hybrid systems with discrete and continuous control signals. In this paper, a new hybrid controller based on Analytical Nonlinear Model Predictive Control (ANMPC) and Particle Swarm Optimization (PSO) for nonlinear hybrid systems is presented. The proposed controller offer sub-optimal solution in reasonable time while respecting the given constraints. The new developed technique is not considered as a computation burden, thus real-time implementation is possible for many hybrid systems. Besides, it can be applied directly to the nonlinear models, avoiding linearization which may lead to inaccurate model and unexpected behaviour. An application of the proposed controller to a three tanks example is presented.

Paper Citation

in Harvard Style

Thomas J. (2015). Integrating Particle Swarm Optimization with Analytical Nonlinear Model Predictive Control for Nonlinear Hybrid Systems . In Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-122-9, pages 294-301. DOI: 10.5220/0005570702940301

in Bibtex Style

@conference{icinco15,
author={Jean Thomas},
title={Integrating Particle Swarm Optimization with Analytical Nonlinear Model Predictive Control for Nonlinear Hybrid Systems},
booktitle={Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2015},
pages={294-301},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005570702940301},
isbn={978-989-758-122-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Integrating Particle Swarm Optimization with Analytical Nonlinear Model Predictive Control for Nonlinear Hybrid Systems
SN - 978-989-758-122-9
AU - Thomas J.
PY - 2015
SP - 294
EP - 301
DO - 10.5220/0005570702940301