Quadrotor controller rankings depend on tuning and timing factors
How Tuning, Reference Preview, Actuator Modeling, and Fallback Policies Change Quadrotor Controller Comparisons: A Reproducible Simulation Study
Robotics
Summary
Comparing quadrotor flight controllers is tricky because how well they perform depends on many details beyond just accuracy. The authors studied multiple controllers under the same simulated conditions and found that factors like controller tuning effort, preview of flight path, how actuator behavior is predicted, fallback strategies, and command processing timing all affect which controller seems best. These findings suggest that controller comparisons should report multiple measures, not just tracking error. The authors offer a checklist to help future studies be more complete and reproducible.
What this means in practice
- •For drone system engineers: Use a multi-factor evaluation approach to fairly compare and select quadrotor controllers that perform well in realistic flight conditions.
- •For robotics software developers: Incorporate timing-aware command scheduling and fallback handling in quadrotor control software to improve real-time reliability and performance.
Authors
Xuan-Quy Dao, Le-Minh Nguyen, Ngoc-Bich Le
Abstract
Quadrotor controller comparisons commonly report a single tracking error, although the resulting ranking also depends on tuning effort, reference information, actuator prediction, optimizer fallback, and command timing. We quantify these effects for cascade PID, hover LQR, boundary-layer sliding-mode control (BL-SMC), linear MPC with LQR fallback (LMPC-F), a preview-enabled LMPC-F, and a geometric $SO(3)$ controller on a common nonlinear plant with rotor gyroscopic coupling, motor lag, and saturation. A primary study (556 episodes at 50~Hz) combines matched twelve-candidate tuning, 400 paired evaluation episodes, and a 48-cell MPC factorial; a confirmation study (1462 episodes) repeats the evaluation on 50 fresh realizations with tuning budgets of 12, 24, and 48 candidates. A matched twelve-candidate tuning protocol changed the mean-RMSE ordering obtained with the fixed baseline settings, moving BL-SMC from last to first; the new ordering recurred on the fresh realizations, and a 48-candidate budget left it unchanged. Reference preview substantially reduced MPC tracking error and, on the figure-eight task, removed fallback activity. On an abrupt step, the outcome depended on the interplay between fallback policy, predictor, and actuator authority. Command timing also changed the outcome: replaying measured command-call delays terminated three of four nominal MPC configurations on a desktop computer and all four on a Raspberry Pi~5, whereas capping the solver at 100 iterations removed every deadline miss in the tested nominal timing profiles. Controller rankings therefore depend not only on tracking accuracy but also on the tuning protocol, reference information, fallback behavior, and implementation timing. We propose a reporting checklist covering tracking accuracy, tuning effort, completion and failure rates, fallback activity, applied-input variation, and command timing.