TEACHING FEEDBACK CONTROL THEORY USING AN INTEGRATING DESIGN PROJECT
Bibliographic record
Abstract
Teaching feedback control theory is challenging because it is important to cover theoretical material intended for fundamental understanding as well as material directly related to industrial practice. One approach to reach this dual objective and prevent control theory from becoming abstract to students is to assign a design project that requires integration of all main concepts taught in class. This approach has been successfully used in eight offerings of the course ChE 3601 Process Dynamics and Control in the Chemical Engineering program at the University of New Brunswick. The one-semester course is an introduction to the dynamic behavior of chemical processes and feedback control loops. The project is assigned at the beginning of the course and involves the design of a feedback control system for a realistic chemical process. The design project is divided into five milestones with deliverables due every two weeks. The final report due at the end of the course must include a description of the proposed system using a P&I diagram, specifications for all control equipment, a dynamic model for all components of the feedback loop, settings for the tuning parameters of the PID controller, and dynamic simulations using Polymath to validate the proposed solution. The course is organized around the project in a manner similar to that used in problem-based learning. The active learning approach used in ChE 3601 provides a deeper understanding of control theory and its application.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".