A Study of the Stability of an Industrial Robot Servo System: PID Control Based on a Hybrid Sparrow Optimization Algorithm
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Bibliographic record
Abstract
Industrial robots can cause servo system instability during operation due to friction between joints and changes in end loads, which results in jittering of the robotic arm. Therefore, this paper proposes a hybrid sparrow search algorithm (HSSA) method for PID parameter optimization. By studying the optimization characteristics of the genetic algorithm (GA) and sparrow search algorithm (SSA), the method combines the global optimization ability of GA and the local optimization ability of SSA, thus effectively reducing the risk of SSA falling into local optimum and improving the ability of SSA to find global optimization solutions. On the basis of the traditional PID control algorithm, HSSA is used to intelligently optimize the PID parameters so that it can better meet the nonlinear motion of the industrial robot servo system. It is proven through experiments that the HSSA in this paper, compared with GA, SSA, and traditional PID, has a maximum improvement of 73% in the step response time and a maximum improvement of more than 95% in the iterative optimization search speed. The experimental results show that the method has a good suppression effect on the jitter generated by industrial robots in motion, effectively improving the stability of the servo system, so this work greatly improves the stability and safety of industrial robots in operation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it