An ARX-Based PID-Sliding Mode Control on Velocity Tracking Control of a Stick-Slip Pi-ezoelectric-Driven Actuator
Bibliographic record
Abstract
Piezoelectric-driven stick slip actuators have been drawn more and more attention in the nano- positioning application due to the high accuracy and theoretical unlimited displacement. However, the hysteresis of piezoelectric actuator (PEA) and the nonlinear friction force between the end- effector and the stage make control of piezoelectric-driven stick slip actuator challenge. This paper presents the development of an autoregressive exogenous (ARX)-based proportional-integral-derive (PID)-sliding mode control (SMC) for the velocity tracking control of the piezoelectric-driven stick slip actuator. Stability is guaranteed by rigorously choosing the appropriate PID parameters and the zero steady state error is achieved. To verify the effectiveness of the proposed method, experiments were carried out on a commercially-available piezoelectric-driven stick slip actuator. The tracking errors were compared with the traditional PID controller, illustrating that in spite of existing of modeling error, the ARX-based PID-SMC is able to better improve the velocity tracking performance of piezoelectric-driven stick slip actuator, compared with the traditional PID controller.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".