Control of a One-Legged Hopping Robot using a Hybrid Neuro-PD Controller
Bibliographic record
Abstract
Primary simulation results of the control of a pneumatically actuated hopping robot along with the mathematical model are presented in. This paper presents the next phase of the research: design of a robust controller for an experimental hopper. Dynamic stability of the hopping robot is investigated using an artificial neural network (ANN)-based proportional-derivative (PD) controller. The hopper's model (i.e. the transfer function of the plant) is identified with the help of an ANN, and then the PD controller is integrated with the trained ANN, so that the plant's output follows a pre-specified reference trajectory. It is evident through computer simulations and experimental results that the proposed controller effectively meets the system's performance requirements, i.e. achieving a user-defined constant jumping height after a number of hops. It is noteworthy that a near zero steady state error and a shorter settling time in the presence of unmodeled system dynamics can be achieved by incorporating an inverse dynamics paradigm into the proposed PD controller in conjunction with an ANN
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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) | 0.000 | 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".