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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".