A PWM-Based Discrete Double Integral Sliding Mode Current Controller Design for a Class-D Amplifier
Bibliographic record
Abstract
This paper presents the design and analysis of a discrete-time, fixed-frequency, pulse-width modulation (PWM)-based double integral sliding mode controller (DISMC) for class-D amplifiers (CDAs), a concept rarely explored in the literature. The proposed controller addresses the unique challenges posed by the time-varying reference signals inherent to CDAs, which complicate stability analysis and gain determination. To overcome these challenges, an alternative approach for stability analysis and gain tuning is introduced, tailored specifically to the dynamic behavior of the AC tracking system. The feasibility of the proposed DISMC is demonstrated through rigorous simulations and experimental evaluations. The controller adopts a double-loop configuration, utilizing both voltage and current errors as state variables, which significantly improves output voltage regulation, transient response, and robustness to line and load variations. Experimental results validate the superior performance of the DISMC under step load changes. For a transition from 200Ω to 20Ω, the DISMC exhibits a voltage deviation of 12V with a regulation time of 40 μs, outperforming the proportional-integral (PI) controller, which shows a deviation of 14V and regulation time of 60 μs. Similarly, for a transition from 20Ω to 200Ω, the DISMC achieves a regulation time of 120 μs compared to 320 μs for the PI 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".