Position Control of Pneumatic Actuators Using Three-Mode Discrete-Valued Model Predictive Control
Bibliographic record
Abstract
A novel discrete-valued model-predictive control (DVMPC) algorithm termed DVMPC2 for the position control of pneumatic actuators using inexpensive on/off valves is presented. DVMPC2 includes a more flexible cost function, an improved prediction strategy, and other improvements. The actuator is a double-acting cylinder with two on/off solenoid poppet valves connected to each chamber. To reduce the switching frequency and prolong the valve life, DVMPC2 directly switches the valves when necessary, instead of using relatively high-frequency pulse-width modulation. Experimental comparisons are made with the state-of-the-art sliding-mode control (SMC) algorithm and the previous DVMPC algorithm. The comparisons are based on the five performance metrics: integral of time-weighted absolute error (ITAE), root mean square error (RMSE), overshoot (OS), steady-state error (SSE), and valve switches per second (SPS). The robustness is evaluated by increasing and decreasing the total mass of the moving components while keeping the controller parameters constant. The experimental results show that the proposed algorithm is superior to the previous DVMPC and outperformed SMC by a wide margin. Specifically, DVMPC2 reduced ITAE by 80%, RMSE by 52%, OS by 43%, and SPS by 20% relative to SMC. There was no clear winner in terms of SSE.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".