Neural network quaternion-based controller for port-Hamiltonian system

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Abstract: In this research article, a control approach for port-Hamiltonian PH systems based in a neural network (NN) quaternion-based control strategy is presented. First, the dynamics is converted by the implementation of a Poisson bracket in order to facilitate the mathematical model in order to obtain a feasible formulation for the controller design based on quaternion NNs. In this study, two controllers for this kind of of system are presented: the first one consists in the controller design for a PH system about its equilibrium points taking into consideration the position and momentum. This mean is achieved by dividing the quaternion neural controller into scalar and vectorial parts to facilitate the controller derivation by selecting a Lyapunov functional. The second control strategy consists in designing the trajectory tracking controller, in which a reference moment is considered in order to drive this variable to the final desired position according to a reference variable; again, a Lyapunov functional is implemented to obtain the desired control law. It is important to mention that both controllers take into advantage that the energy consideration and that the representation of many physical systems could be implemented in quaternions. Besides the angular velocity, trajectory tracking of a three-phase induction motor is presented as a third numerical experiment. Two numerical experiments are presented to validate the theoretical results evinced in this study. Finally, a discussion and conclusion section is provided.

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch

Erschienen in
Neural network quaternion-based controller for port-Hamiltonian system ; volume:57 ; number:1 ; year:2024 ; extent:20
Demonstratio mathematica ; 57, Heft 1 (2024) (gesamt 20)

Urheber
Alsaadi, Fawaz E.
Serrano, Fernando E.
Batrancea, Larissa M.

DOI
10.1515/dema-2023-0131
URN
urn:nbn:de:101:1-2406051713323.798544900445
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
14.08.2025, 10:58 MESZ

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Beteiligte

  • Alsaadi, Fawaz E.
  • Serrano, Fernando E.
  • Batrancea, Larissa M.

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