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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
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System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs.

Luis F Recalde1, Bryan S Guevara2, Christian P Carvajal2

  • 1SISAu Research Group, Facultad de Ingeniería y Tecnologías de la Información y Comunicación, Universidad Tecnológica Indoamérica, Ambato 180103, Ecuador.

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Summary
This summary is machine-generated.

This study introduces a nonlinear model predictive control (NMPC) for hexacopter trajectory tracking in dynamic environments. The controller ensures accurate flight paths while avoiding obstacles, enhancing UAV capabilities.

Keywords:
hexacopter UAVmodel predictive controlobstacles avoidanceoptimizationsystem constraintssystem identification

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Area of Science:

  • Robotics and Control Systems
  • Aerospace Engineering
  • Artificial Intelligence

Background:

  • Accurate trajectory tracking is crucial for unmanned aerial vehicles (UAVs) in dynamic environments, facing challenges from system nonlinearities and obstacles.
  • Current controllers struggle with the complexities of real-time monitoring and obstacle avoidance in unpredictable settings.
  • UAVs offer significant potential across various sectors, including environmental monitoring, safety, and agriculture, necessitating advanced control solutions.

Purpose of the Study:

  • To develop and evaluate a nonlinear model predictive control (NMPC) strategy for hexacopter trajectory tracking in dynamic environments.
  • To incorporate collision avoidance and system constraints within the NMPC framework for enhanced operational safety and reliability.
  • To compare the accuracy of Euler-Lagrange and dynamic mode decomposition (DMD) models for precise system dynamics representation.

Main Methods:

  • Implementation of a nonlinear model predictive control (NMPC) with integrated collision avoidance for hexacopters.
  • Comparative analysis of Euler-Lagrange and dynamic mode decomposition (DMD) models for system dynamics.
  • Inclusion of constraints on velocity, dynamics, obstacles, and tracking error in the optimization control problem (OCP).
  • Validation through computational simulations and real-world experiments using a DJI MATRICE 600 hexacopter.

Main Results:

  • The proposed NMPC controller demonstrated effective trajectory tracking for hexacopters in dynamic environments.
  • Experimental results confirmed the predictive scheme's good performance and ability to regenerate optimal control policies.
  • Simulations showcased the controller's scalability in highly dynamic scenarios, proving its robustness.

Conclusions:

  • The developed NMPC with collision avoidance is a high-performing solution for hexacopter trajectory tracking in complex, dynamic environments.
  • The controller effectively manages system constraints and obstacles, ensuring safe and accurate UAV operations.
  • This approach enhances the potential applications of UAVs in critical sectors requiring precise aerial navigation.