Integrated PID-Based Sliding Mode State Estimation and Control for Piezoelectric Actuators
Bibliographic record
Abstract
Tracking control of piezoelectric actuators (PEAs) has stimulated the development of various advanced control schemes that utilize the feedback of PEA system states for improved control performance. Among them, the one based on the concept of sliding mode has been shown promising due to its robustness to matched uncertainties, but leaving the required state estimation to be desired. Previous studies show that the PEA can be modeled as a linear dynamic system with matched uncertainties. On this basis, this paper presents the development of a novel observer based on the concept of proportional-integral-derivative-based (PID-based) sliding mode, in which the switching function is replaced by a PID regulator. The novel observer, referred to as the PID-based sliding mode observer (PIDSMO), relaxes the observer matching condition as required in the use of the unknown-input observers. The PIDSMO is then integrated with the PID-based sliding mode controller (PIDSMC) to form a novel integrated PID-based sliding mode observer-controller (PIDSMOC) for PEA tracking control. Experiments performed on a PEA showed that the PIDSMO can accurately estimate the PEA states and that the integrated PIDSMOC can achieve better tracking control performances as compared to the PIDSMC with α-β filter control scheme.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".