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Hybrid systems modelling and control using multiple mixed logical dynamical predictive model control: Application to a three-tank spherical system.

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Title: Hybrid systems modelling and control using multiple mixed logical dynamical predictive model control: Application to a three-tank spherical system.
Authors: Benaissa, Tahar1 ben_aissatahar@yahoo.fr, Belazreg, Mohamed Fouzi2 f.belazreg@crna.dz, Halbaoui, Khaled3 kh.halbaoui@crnb.dz, Djaroum, Belaid4 b.djaroum@crnb.dz, Boukhetala, Djamel5 djamel.boukhetala@g.enp.edu.dz
Source: International Journal of Electrical & Computer Engineering (2088-8708). Jun2026, Vol. 16 Issue 3, p1148-1158. 11p.
Subjects: Hybrid systems, Predictive control systems, Quadratic programming, Mathematical optimization
Abstract: This study employs the mixed logical dynamical (MLD) framework for modelling, simulating, and controlling hybrid dynamical systems. Hybrid systems, which combine continuous-time dynamics and discrete logical events, pose significant challenges for conventional control strategies, such as proportional-integral-derivative (PID) controllers, particularly under complex operational constraints. To address these challenges, the MLD formalism provides a unified representation that integrates differential equations, logical rules, and inequality constraints. Based on the MLD model, a multivariable hybrid model predictive control (HMPC) approach is designed to optimize control system performance and operational efficiency over a prediction time horizon. At each sampling time step, a mixed quadratic programming (MIQP) optimization problem is solved online to determine the control law. The proposed control approach is applied to a three-spherical tank system, where simulation and experimental results demonstrate its effectiveness in ensuring stability, minimizing tracking errors, and satisfying physical constraints. These results underscore the relevance of MLD-based predictive control approaches for the optimization and advanced control of complex multivariable hybrid dynamical systems in industrial fields. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: Hybrid systems modelling and control using multiple mixed logical dynamical predictive model control: Application to a three-tank spherical system.
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  Data: <searchLink fieldCode="AR" term="%22Benaissa%2C+Tahar%22">Benaissa, Tahar</searchLink><relatesTo>1</relatesTo><i> ben_aissatahar@yahoo.fr</i><br /><searchLink fieldCode="AR" term="%22Belazreg%2C+Mohamed+Fouzi%22">Belazreg, Mohamed Fouzi</searchLink><relatesTo>2</relatesTo><i> f.belazreg@crna.dz</i><br /><searchLink fieldCode="AR" term="%22Halbaoui%2C+Khaled%22">Halbaoui, Khaled</searchLink><relatesTo>3</relatesTo><i> kh.halbaoui@crnb.dz</i><br /><searchLink fieldCode="AR" term="%22Djaroum%2C+Belaid%22">Djaroum, Belaid</searchLink><relatesTo>4</relatesTo><i> b.djaroum@crnb.dz</i><br /><searchLink fieldCode="AR" term="%22Boukhetala%2C+Djamel%22">Boukhetala, Djamel</searchLink><relatesTo>5</relatesTo><i> djamel.boukhetala@g.enp.edu.dz</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Jun2026, Vol. 16 Issue 3, p1148-1158. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Hybrid+systems%22">Hybrid systems</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+control+systems%22">Predictive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Quadratic+programming%22">Quadratic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: This study employs the mixed logical dynamical (MLD) framework for modelling, simulating, and controlling hybrid dynamical systems. Hybrid systems, which combine continuous-time dynamics and discrete logical events, pose significant challenges for conventional control strategies, such as proportional-integral-derivative (PID) controllers, particularly under complex operational constraints. To address these challenges, the MLD formalism provides a unified representation that integrates differential equations, logical rules, and inequality constraints. Based on the MLD model, a multivariable hybrid model predictive control (HMPC) approach is designed to optimize control system performance and operational efficiency over a prediction time horizon. At each sampling time step, a mixed quadratic programming (MIQP) optimization problem is solved online to determine the control law. The proposed control approach is applied to a three-spherical tank system, where simulation and experimental results demonstrate its effectiveness in ensuring stability, minimizing tracking errors, and satisfying physical constraints. These results underscore the relevance of MLD-based predictive control approaches for the optimization and advanced control of complex multivariable hybrid dynamical systems in industrial fields. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.11591/ijece.v16i3.pp1148-1158
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Predictive control systems
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      – SubjectFull: Quadratic programming
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      – SubjectFull: Mathematical optimization
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              Text: Jun2026
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              Y: 2026
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