A novel sliding mode control based on qubit rotation angle for efficient manipulation of robotic arms
Bibliographic record
Abstract
A novel control strategy, termed Quantum Sliding Mode Control (QSMC), integrates a qubit-inspired spin angle formulation into the sliding surface dynamics of a nonlinear robotic manipulator presented in this research. While Classical Sliding Mode Control (SMC) is robust, it suffers from discontinuous switching that causes chattering and compromises energy efficiency. The proposed QSMC framework replaces the discontinuous signum function with a continuous sinusoidal modulation derived from the normalized state of a single qubit on the Bloch sphere, controlled by the geometric ratio of tracking error, allowing for real-time adjustment of control effort. The controller is validated through extensive simulations on a six-degree-of-freedom robotic arm under both nominal and perturbed conditions. Across six joints, the proposed QSMC reduced average control energy by 75.41 % and high-frequency content by 96.12 %, with 59.34 % and 95.84 % reductions under 10 % disturbances, while maintaining trajectory tracking accuracy. Results show that QSMC maintains tracking precision comparable to classical SMC while significantly reducing high-frequency actuation, spectral energy, representing near-invariant control effort in the presence of external disturbances, indicating strong adaptability and energy efficiency. By incorporating quantum-geometric reasoning into a deterministic nonlinear control law, the novel QSMC provides discrete-state abstraction with continuous-time dynamics, offering a robust and physically interpretable alternative to both conventional and AI-enhanced SMC techniques.
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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 | 0.000 |
| 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".