Robust Trajectory-Tracking for a Bi-Copter Drone Using INDI: A Gain Tuning Multi-Objective Approach
Bibliographic record
Abstract
This paper presents an optimized robust trajectory control system for an autonomous tiltrotor bi-copter based on an incremental nonlinear dynamic inversion (INDI) strategy combined with a set of PID/PD controllers. The methodology includes a lower level, fast attitude control action using an incremental nonlinear dynamic inversion (INDI) strategy, which is driven by a higher level, slow trajectory control action that uses nonlinear dynamic inversion (NDI). The nonlinear dynamic model of the drone is derived, and the basis of the motion and the design of the attitude and position stabilizing controllers are discussed. To develop and test the suggested controller, a circle-shaped flight profile is simulated. The linear control providing inputs to the NDI and INDI controllers is tuned via a novel multi-objective optimization auto-tuning method using the non-dominated sorting genetic algorithm II (NSGA-II). The tracking and disturbance rejection optimization is achieved via the use of the integral of time multiplied by the absolute error (ITAE) and the integral of the square of the error (ISE) objective functions, which are optimized concurrently. The simulation results reveal that the proposed control design outperforms the traditional dynamic inversion controller design and demonstrate that the developed INDI + PID/PD controller possesses exceptional accuracy and performance, enabling the tiltrotor bi-copter to track the given trajectory. Furthermore, the paper shows that the proposed controller produces 40% lower overshoot and settling time as measured with respect to previous backstepping controllers reported in the literature. The robustness of the controller is validated through diverse tests where the aircraft is subjected to external (wind gust) disturbances.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".