Controller Implementation of a Balancing Robot through a Dynamic Model with Acceleration Control Input
Bibliographic record
Abstract
In this paper, we propose a new dynamic model of the balancing robot, and present the implementation method and results of the controller based on this model.The model equation of the system is derived through the Lagrangian approach.The model for tracking control of balancing robot is proposed, of which tracking reference is added as state, and implemented through LQR controller.The dynamic model proposed in this paper has the acceleration of robot body as the control input, unlike the conventional model which uses the torque or voltage as the control input.The acceleration from the LQR controller is transformed to the velocity reference of a motor and this value is applied to the real system through motor velocity controller.The motor velocity controller could be separated from the LQR controller making control reference.Therefore, the entire control could be easily implemented by changing only the motor velocity controller depending on the motor used in a balancing robot.The performance of the entire controller can be improved by controlling velocity accurately.To verify usefulness and reliability of this proposed model, two balancing robots, one of which uses step motors and the other of which uses DC motors, are implemented and results for balancing and tracking control are presented.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".