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Efficient Nonlinear Model Predictive Control for UAV Trajectory Tracking
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AI
DOI：10.1007/978-3-032-08381-4_25.png)
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Abstract
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This paper proposes an efficient Nonlinear Model Predictive Control (NMPC) framework for precise trajectory tracking of quadrotor Unmanned Aerial Vehicles (UAVs). The quadrotors nonlinear dynamics are modeled using the Euler-Lagrange formulation, capturing six degrees of freedom while considering system constraints. The NMPC optimizes performance via a quadratic cost function over a finite prediction horizon, integrating advanced trajectory generation and real-time computation capabilities. Simulation results validate the proposed controllers ability to track accurately across challenging scenarios, maintain stability under disturbances, and ensure computational efficiency for real-time applications. This work provides a practical and robust solution to key challenges in autonomous UAV control.
Keywords:
Nonlinear Model Predictive Control
Quadrotors
UAVs
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