Adaptive unscented Kalman filter-based online slip ratio control of wheeled-mobile robot
Bibliographic record
Abstract
This paper presents an adaptive unscented Kalman filter (AUKF)-based sliding mode control (SMC) method for effective tracking of the slip ratio applicable to wheeled mobile robots (WMR). The adaptive unscented Kalman filter is developed to estimate the vehicle longitudinal velocity and the wheel angular velocity in the presence of system parameter variation and disturbances. An adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the unscented Kalman filter context. Furthermore, a sliding mode controller is designed for slip tracking control of the uncertain nonlinear dynamical system in the presence of model uncertainties, parameter fluctuations, and disturbances. The effectiveness of the controller has been verified by carrying out simulation studies. The controller is able to provide accurate reference slip tracking of the mobile robot, despite uncertainties present in the robot/wheel dynamics and changing terrain conditions. It is also demonstrated that the adaptive concept of AUKF leads to better results than the unscented Kalman filter in estimating the vehicle velocity which is difficult to measure in actual practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".