A comparative study of MPC and optimised PID control
Bibliographic record
Abstract
The paper presents a comparative study between the performance of model predictive control (MPC) and optimised proportional-integral-differential (PID) control on different systems. An objective function derived from rise time, settling time, percentage overshoot, and steady-state error is minimised in order to obtain the gains of the PID controller. The bacterial foraging (BF) algorithm is used to tune the parameters of the PID controller through performance optimisation of the system. System performance characteristics are compared to another controller designed based on MPC. The rise time, settling time, percentage overshoot, integral absolute error (IAE), and integral time-multiplied absolute error (ITAE) of the controllers are compared when acting on systems of different orders. Controller testing and performance comparison are carried out on first, second, third, forth, and fifth order systems. Results indicate that MPC consistently outperforms optimised PID control in all cases.
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How this classification was reachedexpand
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 | 0.000 |
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 teacher head, 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".