Fuzzy logic temperature controller for small robots
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
Malfunctions in a working robot system are serious factors which can result in deviations from the desired goals. Among these, temperature increase over safe values is one of the most important factors that can limit the usage of available energy. The heat generated from motors and batteries can damage the electrical systems and affect the overall performance. This results in the need to control and maximize the utilization of supplied energy from batteries. In this paper, the authors designed an intelligent system to control temperature thus preventing system malfunction due to overheating in insulated robots, such as underwater robots. This temperature controller works in parallel with the robot motion controller. The proposed temperature control system uses fuzzy logic as a control layer system. It has been adopted to avoid system errors and efficiently use the energy available. The system is developed using fans, heaters and fuzzy logic circuits for achieving an energy efficient robot with long-term high performance.
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) | 0.000 | 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) | 0.000 | 0.001 |
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