Dynamic Inversion Based Higher Order Model Reference Adaptive Control of Scalar Systems with Time Varying Parameters
Bibliographic record
Abstract
Dynamic Inversion is a promising control scheme, especially for systems with large parametric variations and high nonlinearities. In aerospace control, it is usually used to control aircraft with large flight envelope and highly nonlinear aerodynamics. Traditionally, this was catered for by using Gain Scheduling, which is a time consuming process. One major disadvantage of Dynamic Inversion is that it requires precise knowledge of system dynamics and parameters. To overcome this, some adaptation scheme could be used to estimate the parameters and uncertainties online. Model Reference Adaptive Control (MRAC) is a widely used adaptive control method in such scenarios. Conventionally, MRAC operates under the assumption that the reference or desired dynamics mirror those of the system. Yet, Dynamic Inversion transcends this constraint, often necessitating desired dynamics of higher order than that of the system itself. In this paper, we derive MRAC control laws accommodating higher order desired dynamics, for scalar systems with constant parameters. Subsequently, we extend this framework to systems featuring time-varying parameters, leveraging the concept of congelation of variables. We then apply these proposed control laws to tackle a pertinent pitch control problem, followed by simulation results and discussion.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".