An Improved Fractional Filter Fractional IMC-PID Controller Design and Analysis for Enhanced Performance of Non-integer Order Plus Time Delay Processes
Bibliographic record
Abstract
The objective of this work is to design a fractional filter fractional order PID controller for non-integer order plus time delay (NIOPTD) systems using fractional internal model control (IMC) filter structure. The novelty of the work lies in identifying the higher order fractional IMC filter structure using a systematic analytical procedure based on the minimization of integral absolute error (IAE). The resulting controller consists of a fractional filter term and a fractional PID controller. The tuning parameters are identified based on the minimum value IAE for a fixed robustness (Ms). Simulations are carried out for servo and regulatory response and it was found that an enhanced performance is observed with the proposed controller in terms of low IAE and ITAE. Uncertainties in the process parameters are considered to check the robustness and the stability is assessed with robust stability analysis. The results indicate that the closed loop system with the proposed controllers is robustly stable. In addition, fragility analysis has been done for uncertainties in the controller parameters. The major contribution of this work is the analytical design procedure for identification of optimum fractional IMC filter structure with higher order pade's approximation for timed delay.
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.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".