<scp>A</scp> novel <scp>proportional‐integral‐derivative</scp> ‐type iterative learning control strategy based on <scp>2D</scp> model predictive iterative learning control optimization for batch processes with partial actuator failures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract For control problems involving partial actuator failures during batch processes, this paper proposes a novel proportional‐integral‐derivative (PID)‐type iterative learning control strategy based on two‐dimensional model predictive iterative learning control optimization. First, a two‐dimensional extended non‐minimal state space model was constructed using actuator outputs, process variable outputs, and tracking error information, providing more freedom for subsequent controller design. At the same time, a setpoint learning strategy was constructed by introducing historical batch error information. Secondly, a novel model was constructed by combining the two‐dimensional extended non‐minimal state space model and the setpoint learning strategy, which significantly improved the iterative learning ability of the corresponding controller. Furthermore, a novel PID‐type iterative learning controller is constructed by combining an incremental PID controller and a traditional PID‐type iterative learning controller. Finally, the designed control law is used to optimize the parameters of the PID‐type iterative learning controller in real time, ensuring that the control system can simultaneously achieve control performance along the time axis and convergence performance along the batch axis. Based on the model mismatch in the injection moulding machine holding pressure control system, the effectiveness of the proposed strategy was verified by considering two types of partial actuator failures.
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.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it