Sliding Mode Iterative Learning Control With Iteration-Dependent Parameter Learning Mechanism for Nonlinear Systems and Its Application
Bibliographic record
Abstract
In this study, the data-driven sliding-mode iterative learning tracking control problem of a piezoelectric-actuated micro-positioning (PAMP) stage is investigated. To improve the convergence performance of the data-driven sliding mode iterative learning control (DDSILC) method, a novel iteration-dependent parameter learning mechanism is proposed. Subsequently, an enhanced DDSILC (E-DDSILC) scheme is constructed. The novel parameter-learning mechanism is designed such that the tracking error in time-varying systems can converge to zero in the time domain at the final iteration, and to significantly improve the transient performance of the system. Additionally, the effect of control parameters on the convergence performance is analyzed, which enables the parameters to be adjusted reasonably and efficiently. Several comparison experiments are conducted on the PAMP stage to verify the effectiveness of the proposed control approachNote to Practitioners—Owing to the gradual industrial development toward the high-end manufacturing, many products, such as vascular robots and precision chips, have reached the micro/nano level of accuracy. The piezoelectric-actuated micro-positioning (PAMP) stage has been widely used in high-precision fields, such as fluorescence microscopy, nanoimprint lithography, and laser interferometry, owing to its fast response and ability to generate micro-nano displacements. However, because of the hysteresis and other nonlinear characteristics existed of the PAMP stage, advanced control algorithms are required to address the nonlinearity and satisfy the requirements of high-precision control. When the PAMP stage is used for precision manipulation tasks, such as nanolithography and micro/nanoimaging, the advanced control algorithms present the following restrictions: high dependence on offline models and the necessity to select parameters via trial-and-error method. These limitations render it difficult to implement control approaches. Hence, this study proposes an E-DDSILC scheme, which adopts the dynamic linearization to obtain the nonlinear information of the system. Unlike the conventional DDSILC method, the proposed scheme with enhanced iterative learning mechanism guarantees error convergence in the time domain. Furthermore, the effects of the main parameters on the transient and steady-state performances are investigated. As an offline model is not required for the proposed method and the effects of the parameters are explicit, the operation time is reduced and the feasibility of the controller in practical implementation is guaranteed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".