Multi-Input Fuzzy control of an inverted pendulum using an armature controlled DC motor
Bibliographic record
Abstract
This paper presents a design methodology to stabilize a class of multi-variant nonlinear system after a high disturbance occurs. It investigates application of Takagi-Sugeno type fuzzy controller (T-S-FC) to an inverted pendulum mechanism, actuated by an armature-controlled DC electrical motor. Fuzzy controllers use heuristic information in developing design methodologies for control of non-linear dynamic systems. This approach eliminates the need for comprehensive knowledge and mathematical modeling of the system, and in cases of more complex systems, approximation and simplifications in order to achieve feasible mathematical model is not required. The paper presents the stages of development of the Fuzzy Controller for an inverted pendulum by developing a two-input, Mamdani type system. It evaluates the performance of the system. Then a four-input T-S-FC type is developed. The research compares performances of each controller and presents the result of tests. A model for a DC motor is developed in this study, in order to measure the effect of time delays and response time caused by inherent properties of the physical system. The final part will demonstrate the complete operational system with the DC electrical motor included in the test system.
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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) | 0.000 | 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 | 0.001 | 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".