Bond Graph Modeling and Simulating of 3 RPR Planar Parallel Manipulator
Bibliographic record
Abstract
A 3-RPR planar parallel robot is a kind of planar mechanisms, which can work at high speed, with high accuracy and high rigidity. In this paper, a multi-body bond graph system will be built for the 3-RPR planar parallel manipulator (PPM), along with 3 PID controllers which give commands to 3 DC motors respectively. The advantage of bond graphs is that they can integrate different types of dynamics systems, the manipulator, the control and the motor can be modelled and simulated altogether in the same process. Bond graph will be established for each rigid body with body-fixed coordinate’s reference frames, which are connected with parasitic elements (damping and compliance) to each other. The PID set-point signals are generated by the explicit inverse kinematic equations. The 3 prismatic lengths constitute the measured feedback signals. In order to make the end-effector reach the ideal position with target orientation, the three links should reach the target lengths simultaneously. In this study, the dynamics simulation of 3-RPR PPM is conducted after building the bond graph system. As the 3 motors are working simultaneously and independently, the end-effector will arrive to the expected position. Finally, the bond graph and control system are validated with the compiled results and 3D animation. Force plot and torque plot will be generated as dynamics performance. Moreover, kinematics of manipulators are also calculated using bond graph. Eventually, bond graphs are shown to be effective in solving not only dynamic but also kinematic problems.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".