Development of a New Robust Controller With Velocity Estimator for Docked Mobile Robots: Theory and Experiments
Bibliographic record
Abstract
The tracking control problem of docked mobile robot systems is challenging due to their nonlinear and underactuated system dynamics as well as limited access to the required states of robots. The majority of the previously developed controllers in the literature are not robust to model uncertainties and are based on the assumption that full states are accessible. In this paper, we develop a new robust tracking controller for a docked nonholonomic mobile robotic system with online velocity estimation. Our proposed controller, composed of sliding mode and robust saturation controllers, is developed to be robust to external disturbances, unmodeled dynamics, and parameter uncertainties. To provide the required states for the controller, a model-aided particle filter estimator is developed to estimate the translational and rotational velocities. We performed extensive experiments to verify the effectiveness of our proposed control and estimation methodologies as well as the integrated system. We also compared our results with some conventional controllers, including the well-known robust sliding mode controller, and demonstrated its superior performance in terms of model uncertainties over all the controllers. The results showed that, compared with sliding mode control, our approach improves the steady-state tracking performance up to 8.3% and 11% for unmodeled dynamics and parametric uncertainties, respectively. Our proposed integrated (controller-estimator) method can be used in uncertain systems with good tracking performance, where accessing velocity directly is not possible.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".