Bibliographic record
Abstract
IntroductionIt has been proven that fuzzy controllers are capable of controlling non-linear systems where it is cumbersome to develop conventional controllers based on mathematical modeling.This chapter describes designing fuzzy controllers for an AC motor run mechanism.It also compares performance of two controllers designed based on Mamdani and Takagi-Sugeno with the conventional control scheme in a short track length, following a high disturbance.Fine and rapid control of AC motors have been a challenge and the main obstacle in gaining popularity in use of AC motors in robots actuators.This chapter reviews how use of intelligent control scheme can help to solve this problem. Cart and Pendulum ProblemDesign and implementation of a system is followed by vigorous testing to examine the quality of the design.This is true in the case of designing control systems.One the classical systems to test quality and robustness of control scheme is inverted pendulum.In recent years, the mechanism of an inverted pendulum on a moving cart has been used extensively and in many different types.The cart and pendulum mechanism has become even more popular since the advent of intelligent control techniques.This mechanism is simple, understandable in operation, and stimulating.It has a non-linear model that can be transformed into linear by including certain condition and assumption in its operation.For the above reasons, inverted pendulum's performance has become a bench mark for testing novel control schemes.In this chapter the focus is on the driving power in balancing the inverted pendulum which is an electrical motor.Traditionally, DC motors are used for this type of tasks.However, in this chapter the focus is on AC electrical motors for producing the torque required for the horizontal movements of the inverted pendulum.A simplified control model for the AC motor is used which includes the motor's equivalent time constant as the crucial parameter in producing rapid responses to the disturbances.In the modeling of fuzzy controllers for the inverted pendulum, the input to the pendulum block is considered to be a torque.This torque is produced by an electrical motor which is not included in the model.That is, the torque is output of the motor.A disadvantage in this modeling is that the electrical motor dynamics is not built-in in the control system independently.On the other hand, not including the electrical motor in the control scheme of the pendulum mechanism provides the freedom to alter the electrical motor and examine the performance of the pendulum with different types of the drive.Here, a simplified model of an AC electrical motor is incorporated into the system.The electrical motor receives its
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".