Improving stability and performance of digitally controlled systems: The concept of Modified Holds
Bibliographic record
Abstract
Digitally controlled systems are getting more and more popular mainly because of their flexibility and convenience but their stability is strongly affected by the time delay introduced by different factors. For common PID type digital controllers, zero-order holds (ZOHs) are commonly employed and the stability characteristics are investigated based on that concept. Mathematical investigations show that higher-order holds may improve the stability and performance of the system and can reduce the steady state errors significantly. This is because the controller tries to learn from the history of the behavior of the system and then predict the behavior for the time period between sampling instances and generate the best possible control force. Furthermore, a new concept of Modified Holds is introduced, which clearly improves the performance of a digital controller. For most control algorithms this does not prolong the processing time significantly (e.g. less than 1%) which can be neglected in the calculations. The varying control force would need an analogue circuitry to follow the proper curve, which might make the controller's electronic circuits more complex. This can be avoided considering that in almost all digital controllers the main core operates at several orders of magnitude higher frequency than that of the control loop itself. Hence, the control force can also be generated digitally at much higher frequencies. In this paper, after investigating the stability of a 1-DoF system equipped with discrete-time PD controller with first and second order holds, the concept of modified holds is introduced and then the results are validated by simulations. Furthermore, the concept is practically implemented on a self-balancing motor bike robot and the experimental results further support the claims of the paper.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".