Adaptives dynamisches Programmieren zur robusten Bahnverfolgung eines landwirtschaftlichen Roboters mithilfe kritischer neuronaler Netze
DOI:
https://doi.org/10.15150/ae.2025.3327Abstract
Die Steuerung der Trajektorienverfolgung für landwirtschaftliche mobile Roboter stellt aufgrund inhärenter nicht-holonomer Einschränkungen und externer Störungen einzigartige Herausforderungen dar. Diese können zu Abweichungen von der gewünschten Bahn führen und die Leistung sowie die Betriebseffizienz des Roboters beeinträchtigen. In diesem Beitrag wird ein fortschrittliches, lernbasiertes Steuerungsframework für die robuste Bahnverfolgung von landwirtschaftlichen Robotern mit Ackermann-Lenkmechanismen vorgestellt. Mithilfe von adaptivem dynamischem Programmieren (ADP) und einem kritischen neuronalen Netz (Critic Neural Network) bewältigt die vorgeschlagene Methode externe Störungen, einschließlich Raddurchdrehens, das in landwirtschaftlichen Umgebungen häufig auftritt. Das kritische neuronale Netz löst die Hamilton-Jacobi-Isaacs (HJI)-Gleichung, wodurch der Regler die nahezu optimale Steuerungspolitik in Echtzeit erlernen und sich an Umweltstörungen anpassen kann. Die Gewichte des neuronalen Netzes werden online durch ein adaptives Gesetz aktualisiert, was kontinuierliches Lernen und Anpassung während des Betriebs gewährleistet. Darüber hinaus werden umfassende Simulationsstudien präsentiert, um die Wirksamkeit des vorgeschlagenen Frameworks zu bewerten. Die Ergebnisse zeigen erhebliche Verbesserungen der Trajektorienverfolgungsleistung im Vergleich zu bestehenden Steuerungsmethoden, insbesondere in Szenarien mit erheblichen Unsicherheiten und Störungen.
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