Almost Disturbance Decoupling for A Class of Underactuated Nonlinear Systems with Unknown Disturbances by Backstepping
Bibliographic record
Abstract
In response to the lack of backstepping technology and almost disturbance decoupling (ADD) method in the under-actuated field, this paper proposes a new ADD controller design strategy for a class of underactuated nonlinear systems with unknown disturbances by using backstepping technology. Firstly, an error system is formed via a special state feedback, which is based on a partial differential equation (PDE). It can be deter-mined that this process is essentially the backstepping technique. Then, a novel error transformation is developed for the non-strict feedback problem of the formed error system. An ADD controller for the error system is designed by backstepping technology. Finally, the stability and ADD problem of the proposed strategy are verified according to converse Lyapunov theorem. The inertia wheel pendulum (IWP) with external matched disturbances is utilized to explain the effectiveness of the proposed strategy. From the simulation results, it can be seen that the proposed scheme possesses excellent control performance compared to previous underactuated control methods.
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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) | 0.001 | 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 | 0.000 |
| 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".