Hardware-in-the-loop simulator for research on fault tolerant control of electrohydraulic flight control systems
Bibliographic record
Abstract
This paper describes the development of a hardware-in-the-loop (HIL) simulator to support the design and testing of novel fault tolerant control and condition monitoring schemes for fluid power systems emphasizing flight control applications. The simulator uses a distributed architecture to share, in a synchronized manner, the demanding computational load associated with the real-time simulation amongst a number of desktop workstations connected by a dedicated Ethernet network. The simulator runs a high-fidelity model of the F-16 fighter aircraft that is augmented in this paper by the addition of realistic nonlinear models of the hydraulic flight control surface actuators and a model of the nonlinear control surface aerodynamic loads. A specially designed state-of-the-art hydraulic test rig, which has the capacity to experimentally simulate common failure modes of a typical fluid power circuit, is used to emulate a F-16 horizontal tail actuator. The experimental actuator can thus be exercised against the realtime simulation of a F-16 aircraft operating under a variety of normal or faulty conditions. To add further realism to the simulation, a second experimental hydraulic actuator is used to generate the aerodynamic disturbing load. Novel fault tolerant control and diagnosis algorithms can therefore be verified in a realistic application scenario. Pilot-in-the-loop simulations are supported by the inclusion of a graphical visualization of the aircraft motions. The results of a typical HIL experiment, for a normally functioning hydraulic system, are presented to illustrate the operation of the simulator
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".