EMPC-Based Flight Control and Collision-Free Path Planning for a Quadrotor With Unbalanced Payload
Bibliographic record
Abstract
In this article, a robust explicit model predictive control (EMPC) flight scheme is investigated for a quadrotor. MPC is widely recognized for its control effectiveness, but the computational complexity involved in solving online optimization problems, particularly when applied to fast systems, poses a significant challenge. To enable real-time MPC implementation on quadrotor systems, we propose a novel dual-layer control architecture integrating EMPC, strategically relocating the computationally intensive optimization process to offline computation. The outer loop computes reference roll and pitch angles, while the inner loop employs an EMPC framework to achieve fast attitude tracking considering state and actuator constraints. Moreover, integral sliding mode control (ISMC) is integrated to mitigate the effects of uncertainties, such as unbalanced payloads. The recursive feasibility is guaranteed for the proposed flight control method if the initial states are in the feasibility set, and the Lyapunov stability analysis is conducted. In addition, we develop a polynomial trajectory planning algorithm for the quadrotor in (3-D) space. We employ our previous result, the bidirectional guidance informed trees (BIGIT*) algorithm, to obtain a sequence of collision-free waypoints, and utilize the minimum-snap technique to generate a smooth path. Finally, experimental results demonstrate the effectiveness of the proposed methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".