Input and State Constrained Nonlinear Predictive Control - Application to a Levitation System

Joanna Zietkiewicz

Abstract

The subject of the article concerns a constrained predictive control with feedback linearization (FBL) applied for multiple-input and multiple-output (MIMO) system. It relies on finding a compromise in every step between feasible and optimal linear quadratic (LQ) control by minimization of one variable. Behaviour of model signals in function of minimized variable is investigated, in order to assure the optimality of the solution. LQ control based applications for feedback linearized models do not meet the problem of choosing weights in linear quadratic cost function. That important problem is solved here by comparison of the cost function with that obtained for the linear approximated system in the operating point. That provides satisfactory behaviour and also justifies the simplified approach relied on minimization of only one variable for MIMO system.

References

  1. Ahmad, A., Saad, Z., Osman, M., Isa, I., Sadimin, S., and Abdullah, S. (2010). Control of magnetic levitation system using fuzzy logic control. In Proc. of the Second International Conference on Computational Intelligence, Modelling and Simulation, pages 51-56, Bali.
  2. Al-Muthairi, N. and Zribi, M. (2004). Sliding mode control of a magnetic levitation system. Mathematical Problems in Engineering, (2004:2):93-107.
  3. Bachle, T., Hentzelt, S., and Graichen, K. (2013). Nonlinear model predictive control of a magnetic levitation system. Control Engineering Practice, (21):1250-1258.
  4. Dragos, C., Preitl, S., Precup, R., and Petriu, E. (2012). Points of view on magnetic levitation system laboratory-based control education. In HumanComputer Systems Interaction, part II, pages 261- 275. Springer-Verlag, Berlin.
  5. Isidori, A. (1995). Nonlinear Control Systems. SpingerVerlag, London.
  6. Kang, C.-S., Park, J.-I., Park, M., and Baek, J. (2014). Novel modeling and control strategies for a hvac system including carbon dioxide control. Energies, (7):3599-3617.
  7. Khalil, H. (2002). Nonlinear Systems. Prentice Hall, New Jersey.
  8. mls2em (2009). Magnetic Levitation System. User's Manual. printed by InTeCo Ltd.
  9. Poulsen, N., Kouvaritakis, B., and Cannon, M. (2001). Nonlinear constrained predictive control applied to a coupled-tanks apparatus. In IEE Proc. of Control Theory and Applications, volume 148, pages 17-24.
  10. Yang, Z.-J. and Tateishi, M. (2001). Adaptive robust nonlinear control of a magnetic levitation system. Automatica, (37:7):1125-1131.
  11. Zietkiewicz, J. (2012). Constrained predictive control of mimo system. application to a two link manipulator. In Proc. of the 9th International Conference on Informatics in Control, Automation and Robotics, pages 293-298.
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Paper Citation


in Harvard Style

Zietkiewicz J. (2014). Input and State Constrained Nonlinear Predictive Control - Application to a Levitation System . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-039-0, pages 274-279. DOI: 10.5220/0005055502740279


in Bibtex Style

@conference{icinco14,
author={Joanna Zietkiewicz},
title={Input and State Constrained Nonlinear Predictive Control - Application to a Levitation System},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2014},
pages={274-279},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005055502740279},
isbn={978-989-758-039-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Input and State Constrained Nonlinear Predictive Control - Application to a Levitation System
SN - 978-989-758-039-0
AU - Zietkiewicz J.
PY - 2014
SP - 274
EP - 279
DO - 10.5220/0005055502740279