An Output-Tracking-Based Discrete PID-Sliding Mode Control for MIMO Systems
Bibliographic record
Abstract
Due to its ability to reject disturbance, sliding mode control (SMC) has been widely employed in various control applications with the presence of uncertainty and/or disturbance. However, chattering, caused by the switching function used in SMC, can greatly deteriorate its performance, and thus, limit its applications. In addition to chattering, the application of SMC to a multi-input–multi-output (MIMO) system becomes more challenging due to the coupling effects between the control variables. This paper presents the development of an output-tracking-based discrete proportional-integral-derivative SMC (PID-SMC) for MIMO systems, in which the problem of output tracking is defined to the one of state tracking by using the model reference approach. The developed control scheme allows for both achieving the zero steady-state error and eliminating the chattering problem. For validation, experiments were performed on a commercially available three degrees-of-freedom nanopositioning stage with the developed control scheme, as compared to a traditional PID controller. Experimental results illustrate that with the developed control scheme, the positioning performance of the stage can be significantly improved, including the zero steady-state error and eliminated chattering.
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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 | 0.001 |
| 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".