Speed control strategy for power line inspection robot servo system considering time-varying parameters
Bibliographic record
Abstract
Most servo systems in power line inspection robots consist of a motor, an independent joint, and a load. In the process of crossing obstacles, the parameters in the servo systems have conspicuous time-varying properties due to the posture changes. The time-varying properties of dynamic parameters and the flexibility of the load would cause the rotation speed of the inspection robot to fluctuate, thereby affecting the motion accuracy. In this paper, the pole placement strategy was proposed to optimize the parameters in the proportional integral (PI) controller. The optimal controller parameters were selected in different postures to ensure steady speed output in the inspection robot servo system. First, the dynamic equations of the inspection robot servo system were established. Both joint flexibility and load flexibility were considered in the modeling process. Then, the Arnoldi algorithm was used to reduce the order of the servo system, and the transfer function from the speed to the drive torque was obtained. Next, the controller parameters were optimized using the pole placement method. By reasonably selecting the pole damping coefficient, the inspection robot could obtain a stable speed output. Finally, the numerical analysis and speed control of the inspection robot in different postures were analyzed. The results showed that the control strategy of pole placement could achieve a stable rotation speed for the inspection robot.
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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 | 0.000 |
| 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".