Tracking control for non-holonomic mobile manipulator using decentralised control strategy
Bibliographic record
Abstract
This paper presents a tracking control strategy for a non-holonomic mobile manipulator using a decentralised control strategy. The mobile manipulator is viewed as an interconnection of two subsystems - a non-holonomic mobile platform subsystem and a holonomic manipulator subsystem. First, a kinematic controller of the two-wheel driven mobile platform is developed to obtain a desired velocity. Second, a distributed control strategy is developed in order to track a desired trajectory in the joint space. This desired trajectory is obtained from the workspace trajectory using the inverse kinematics. The distributed control strategy consists of controlling the manipulator, starting from the last joint and going backwards until the first joint. Each joint is controlled while assuming that the remaining joints and the platform are stable and follow their desired trajectories. The stability of the system is proved using Lyapunov theory. These controllers are tested on a three degrees-of-freedom mobile manipulator and compared with the computed torque approach. The experimental and simulation results present a good tracking which shows the effectiveness of this control strategy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".