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Design of explicit model predictive control for constrained linear systems with disturbances

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  • Control Theory
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Abstract

On-line model predictive control approaches require the online solution of an optimization problem. In contrast, the explicit model predictive control moves major part of computation offline. Therefore, eMPC enables one to implement a MPC in real time for wide range of fast systems. The eMPC approach requires the exact system model and results a piecewise affine control law defined on a polyhedral partition in the state space. As an important limitation, disturbances may reduce performance of the explicit model predictive control. This paper presents efficient approach for handling the problem of using eMPC for constrained systems with disturbances. It proposes an approach to improve performance of the closed loop system by designing a suitable state and disturbance estimator. Conditions for observability of the disturbances are considered and it is depicted that applying the disturbance’s estimation leads to rejection of the response error. It is also shown that the proposed approach prevents the reduction of feasible space. Simulation results illustrate the advantages of this approach.

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Authors and Affiliations

  1. Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

    Mohammad Ali Mohammadkhani & Ali Akbar Jalali

  2. Department Engineering, Faculty of Electrical Engineering, University of Zanjan, Zanjan, Iran

    Farhad Bayat

Authors
  1. Mohammad Ali Mohammadkhani
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  2. Farhad Bayat
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  3. Ali Akbar Jalali
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Corresponding author

Correspondence to Ali Akbar Jalali.

Additional information

Recommended by Associate Editor Izumi Masubuchi under the direction of Editor PooGyeon Park.

Mohammad Ali Mohammadkhani received his Master degree in Mechatronics engineering from K.N. Toosi University of Technology in 2010. He is currently working towards a Ph.D. degree at the Iran University of Science and Technology. His research interests include applying robust explicit Model Predictive control, reference tracking for state-feedback controllers, model predictive control, and aerospace applications of control.

Farhad Bayat was born in 1981 in Zanjan, Iran. He is an assistant professor at the Department of Engineering at the Zanjan University, Zanjan, Iran. He received his M.Sc. degree in 2006 and Ph.D. degree in 2011, both in Electrical Engineering from the Iran University of Science and Technology, Tehran, Iran. Since 2008 he is serving as a lecturer in the Department of Engineering, University of Zanjan where he has been appointed as an assistant professor in 2011. He has been a research visitor at the Department of Engineering Cybernetics at the Norwegian University of Science and Technology, Trondheim, Norway, in 2010. He is a member of the IEEE community and he has served as a review for several international journals, e.g., Automatica, Control Engineering Practice and IEEE Transactions on Circuits and Systems. His research interests include model predictive control, multi-parametric programming, optimization based control, and aerospace applications of control.

Ali Akbar Jalali received his M.Sc. in Electrical Engineering from the University of Oklahoma, USA, in 1988. He sequentially earned his Ph.D. and post doctoral in Electrical Engineering, from West Virginia University in 1993 and 1994, respectively. From 1993 to 1994 he has been a visiting assistance professor in West Virginia University. He is an adjunct professor in LCSEE from April 2002 and at the same time he is an associate professor at the Iran University of Science and Technology. He is a member of the IEEE and Iranian Society of Instrument & Control Engineering (ISCI). His research interests include robust control and Hinf filtering theory and design, optimal control, and optimization.

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Mohammadkhani, M.A., Bayat, F. & Jalali, A.A. Design of explicit model predictive control for constrained linear systems with disturbances. Int. J. Control Autom. Syst. 12, 294–301 (2014). https://doi.org/10.1007/s12555-013-0058-0

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  • DOI: https://doi.org/10.1007/s12555-013-0058-0

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