An Introspective Learning Algorithm that Achieves Robust Adaptive Control of a Quadrotor Helicopter
Bibliographic record
Abstract
This paper looks at applying a novel robust adaptive control algorithm to achieve stable adaptive control of a quadrotor helicopter. In direct adaptive control, drift of adaptive parameters to large magnitudes can lead to control signal chatter and bursting behavior. Drift is likely to happen when systems are affected by disturbances, for quadrotor helicopters when picking up payloads or flying in windy conditions. Traditional methods to stop weight drift rely on simple mathematical modifications of the parameter/weight update laws, by limiting or halting weight updates in a simplistic fashion. However, performance may be limitedfor a quadrotor helicopter flying in windy conditions performance may be far from adequate. This paper proposes a design of an algorithm to supervise weight updates in a direct adaptive control scheme when using the Cerebellar Model Arithmetic Computer (CMAC) as the nonlinear approximator. The new algorithm makes an introspective decision on when to halt weight updates, based on the perceived affect of each weight update on the error within the local domain of each CMAC basis function. In fact, each domain casts a weighted vote as to whether it perceives a beneficial effect from the weight update. An addition of the weighted votes determines whether the update will be kept in permanent memory or not. Simulation results with a quadrotor helicopter show this novel approach can halt weight drift, achieving both high performance and stability, in the case of uncertain payload and large unmeasured sinusoidal disturbance a situation where the common e-modification robust adaptive weight update cannot achieve a practical result.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".