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<p>The precision of quadrotor trajectory tracking faces technical complexity because of system nonlinear dynamics and wind disturbances. Accurate tracking of quadrotor trajectories remains challenging due to wind and nonlinear dynamics. Existing controllers, including PID, SMC, and standard MPC, often perform poorly under uncertainty due to chattering, model dependence, limited robustness, and fixed-weight tuning issues. The proposed work develops a flatness-based nonlinear model predictive control (FNMPC) framework optimized through adaptive particle swarm optimization (APSO) for improving quadrotor stability as well as trajectory tracking accuracy under Von Kármán turbulence wind disturbances. The APSO facilitates automatic NMPC parameter optimization compared to traditional NMPC, which needs manual weight and constraint adjustments because this process identifies parameter configurations that minimize tracking errors while improving system stability. Also, a variation of PSO, namely standard PSO, is explored to assess its impact on increasing the speed of convergence as well as control performance improvement. Real-world flight conditions are supported by this approach because it includes the Von Kármán model to simulate realistic wind disturbances. The PSO-optimized NMPC attains superior performance than regular NMPC based on simulation outcomes through fast convergence speed, minimal steady-state error of.007&#xa0;m, tracking accuracy of about 99.9%, and enhanced disturbance rejection. APSO provides a superior approach to adaptive control than standard PSO operates in changing turbulence conditions. The trajectory tracking performance is evaluated by extensive simulations that produce visual results to illustrate accuracy with different initial conditions and wind velocity conditions. The presented research develops an optimized NMPC framework for wind-resistant quadrotor flight, demonstrating superior robustness compared to established control approaches.</p>

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Optimization enhanced flatness-based nonlinear model predictive controller for quadrotor

  • 期刊论文

  • 发表日期：

    2026年6月2日

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  • B. B. Binoy,
  • S. Sreeja,
  • Radhika Raveendran

摘要

The precision of quadrotor trajectory tracking faces technical complexity because of system nonlinear dynamics and wind disturbances. Accurate tracking of quadrotor trajectories remains challenging due to wind and nonlinear dynamics. Existing controllers, including PID, SMC, and standard MPC, often perform poorly under uncertainty due to chattering, model dependence, limited robustness, and fixed-weight tuning issues. The proposed work develops a flatness-based nonlinear model predictive control (FNMPC) framework optimized through adaptive particle swarm optimization (APSO) for improving quadrotor stability as well as trajectory tracking accuracy under Von Kármán turbulence wind disturbances. The APSO facilitates automatic NMPC parameter optimization compared to traditional NMPC, which needs manual weight and constraint adjustments because this process identifies parameter configurations that minimize tracking errors while improving system stability. Also, a variation of PSO, namely standard PSO, is explored to assess its impact on increasing the speed of convergence as well as control performance improvement. Real-world flight conditions are supported by this approach because it includes the Von Kármán model to simulate realistic wind disturbances. The PSO-optimized NMPC attains superior performance than regular NMPC based on simulation outcomes through fast convergence speed, minimal steady-state error of.007 m, tracking accuracy of about 99.9%, and enhanced disturbance rejection. APSO provides a superior approach to adaptive control than standard PSO operates in changing turbulence conditions. The trajectory tracking performance is evaluated by extensive simulations that produce visual results to illustrate accuracy with different initial conditions and wind velocity conditions. The presented research develops an optimized NMPC framework for wind-resistant quadrotor flight, demonstrating superior robustness compared to established control approaches.