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<p>This paper introduces a comprehensive and modular control strategy for a quadcopter employing six decoupled fuzzy logic controllers, each dedicated to controlling one degree of freedom: roll, pitch, yaw, altitude, and horizontal positions (x, y). The key novelty of this work lies in the integration of a novel multi-objective Self-Adaptive Bonobo optimizer to optimize eighteen controller gains simultaneously, leveraging Pareto-front principles for the first time in this context to effectively balance tracking accuracy with control effort. The proposed controllers robustly handle the nonlinear and coupled dynamics of the quadcopter while maintaining resilience against disturbances and model uncertainties. The obtained results validate the efficacy of this approach, demonstrating significant improvements, including up to a 51% reduction in root mean square error and 76% reduction in mean absolute error in lateral motion over baseline configurations, without compromising altitude stability and with enhanced response times compared to random gains selection. Furthermore, the control scheme is validated via real-time implementation on a dSPACE 1202 Processor-in-the-Loop platform under aggressive trajectory conditions. Finally, a novel conceptual framework is proposed for deploying these optimized fuzzy controllers directly on embedded flight hardware under aggressive trajectories, creating efficient autonomous onboard control and representing a practical step towards real-world Unmanned Aerial Vehicles applications.</p>

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Six degree of freedom quadcopter stabilization using self adaptive bonobo Pareto multi objective FLC with processor in the loop validation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月17日

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  • Aissa Benhammou,
  • Mohamed Amine Hartani,
  • Fatma A. Hashim,
  • Walaa Alayed

摘要

This paper introduces a comprehensive and modular control strategy for a quadcopter employing six decoupled fuzzy logic controllers, each dedicated to controlling one degree of freedom: roll, pitch, yaw, altitude, and horizontal positions (x, y). The key novelty of this work lies in the integration of a novel multi-objective Self-Adaptive Bonobo optimizer to optimize eighteen controller gains simultaneously, leveraging Pareto-front principles for the first time in this context to effectively balance tracking accuracy with control effort. The proposed controllers robustly handle the nonlinear and coupled dynamics of the quadcopter while maintaining resilience against disturbances and model uncertainties. The obtained results validate the efficacy of this approach, demonstrating significant improvements, including up to a 51% reduction in root mean square error and 76% reduction in mean absolute error in lateral motion over baseline configurations, without compromising altitude stability and with enhanced response times compared to random gains selection. Furthermore, the control scheme is validated via real-time implementation on a dSPACE 1202 Processor-in-the-Loop platform under aggressive trajectory conditions. Finally, a novel conceptual framework is proposed for deploying these optimized fuzzy controllers directly on embedded flight hardware under aggressive trajectories, creating efficient autonomous onboard control and representing a practical step towards real-world Unmanned Aerial Vehicles applications.