Control of a One-Legged Hopping Robot using a Hybrid Neuro-PD Controller
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it