Modeling of a fully actuated quadrotor and control optimization using novel chaotic birds of prey algorithm variants

Unmanned aerial vehicles, particularly quadrotors, present significant control challenges due to their nonlinear, underactuated, and coupled dynamics. This paper presents a comprehensive modeling approach for a quadrotor and presents an optimized tuning strategy for a linear quadratic regulator (LQR). To address the complexity of selecting the optimal weighting matrices for the controller, ten novel variants of the birds of prey optimizer (BPO) are developed by integrating distinct chaotic maps to enhance the algorithm’s exploration and exploitation capabilities, avoiding stagnation or premature convergence. These chaotic BPO variants are rigorously evaluated against the standard algorithm in trajectory tracking simulations, covering both soft and aggressive flight scenarios. The optimization framework provides a systematic procedure for tuning the LQR controller, revealing a distinct trade-off between a more aggressive tracking precision and an energy-efficient, conservative control. For soft trajectories, the results demonstrate that Sine and Tent maps-based controllers reduce relevantly the integral of squared error in until 13.26%, and 10.95%, respectively, when compared to the controller designed using the procedure without a chaotic map. For hard trajectories, the Sinusoidal-based controller requires 7.77% less energy for maintaining equivalent tracking performance.