Performance of the optimal nonlinear pid controller for position control of antenna azimuth position system
Bibliographic record
Abstract
This study presents the improvement in the performance of the Proportional-integral-derivative (PID) controller for position control of antenna azimuth position system subjected to external disturbance. The design of the PID controller is developed by adding the arc tan function of error instead of the direct error in an integral part of the PID controller, yielding a Nonlinear PID controller (NPID). A Particle Swarm Optimization (PSO) is used in this study to tune the parameters of the PID and NPID controllers using the Root Mean Square Error (RMSE) cost function. The simulations have been accomplished under the MATLAB/Simulink environment. The simulation results show that a PSO-based NPID controller provides superior steady and transient state performance compared to a PSO-based PID controller. In addition, the effectiveness of the proposed controller is verified via numerical simulation compared to the performance of other controllers with and without external disturbance.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".