Model-Free Force Control of Cable-Driven Parallel Manipulators for Weight-Shift Aircraft Actuation
Bibliographic record
Abstract
We present a novel approach to flight control of weight-shift aircraft by employing a cable-driven parallel robot (CDPR) integrated with adaptive force control based on reinforcement learning. Development of weight-shift aircraft control has been sparse. Despite limited but notable efforts, modeling is hindered by parameter uncertainty stemming from the system’s nonlinear dynamics. The model-free control method introduced in this work operates without relying on the knowledge of the complex dynamics inherent to weight-shift aircraft flight control. An online reinforcement learning technique known as action dependent heuristic dynamic programming (ADHDP) is applied to the problem of coordinating the tension forces across parallel cable-driven actuators. Two adaptive learning agents perform demanded weight-shift maneuvers by coordinating torque commands, without an inverse kinematics model. The online reinforcement learning control is implemented on flight controller hardware with limited computational resources and strict timing constraints, performing real-time experiments on a kinematically equivalent surrogate two-body weight-shift mechanism. After online training in the presence of sustained disturbance events, the adaptive learning agents optimally balance against competing trajectory tracking objectives. The CDPR capably reproduces standard S-turn maneuvers, coordinating simultaneous banking and pitching speed actions. The encouraging experimental results inform future integration of the weight-shift CDPRs toward automatic flight control that is unprecedented for this class of aircraft.
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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 | 0.000 |
| 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".