A Constrained Robust Switching MPC Structure for Tilt-Rotor UAVs Trajectory Tracking Problem
Bibliographic record
Abstract
Abstract In tilt-rotor UAVs, both the fuselage and tilting rotors contribute to the vehicle's rotational motion. Consequently, the system's dynamics rise to a highly-nonlinear system, making it challenging to find feasible and desired control solutions. The common control practices devise a logic-based controller to switch between different flight modes or map the control inputs to the conventional helicopter-type control inputs. However, they fail to provide energy-efficient fast trajectory tracking, especially in the presence of external disturbances. This paper proposes a general-model dynamic formulation and a two-layered constrained Model Predictive Control (MPC) strategy to tackle the trajectory tracking problem for tilt-rotor UAVs. After splitting the vehicle's dynamics into translational and rotational parts, a constrained Linear MPC (LMPC) is designed for the translational dynamic to follow a reference trajectory. We formulate the LMPC as a Quadratically-Constrained Quadratic Problem (QCQP) that leads to a feasible set-point solution for the rotational control layer without violating the physical constraints. Also, an optimizer is designed to generate a thrust vector, which leverages the vehicle's full potential via a continuous transition between the rotation in the fuselage and that in tilting rotors. In the second layer, the nonlinear rotational dynamics are approximated via piecewise affine (PWA) subsystems. A constrained Robust Switching MPC (RSMPC) is then designed to follow the first layer's generated trajectories (Euler angles and the thrust vectors) while preserving the system's stability, feasibility, and robustness in the presence of external disturbances. Furthermore, by providing an augmented dynamic model, this control design would allow for directly incorporating actuator constraints into the problem formulation. We demonstrate the controller's performance and effectiveness via simulations.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".