Modeling and Nonlinear Adaptive Control for Omnidirectional Mobile Robot
Bibliographic record
Abstract
This study presents a complete model for RobotinoMD an omnidirectional mobile robot. This model includes the kinematics and dynamics. It is used for the simulation and design of an adaptive nonlinear control system. The hierarchical control system that is proposed has three levels. The level-one which is the inner loop is used to control the DC motors that drive the robot wheels. A control design method combining an adaptive feedback linearization technique and the Backstepping approach is used to find the controller equation. The adaptation module that is included in the control system maintains the performance of the system in the presence of uncertainties on the inertia, weight and other parameters in the robot dynamics. The level-one controller receives its reference signal from the level-two controller which converts the linear and rotational speeds into desired speeds. This level-two controller receives its reference signal from the level-three controller which is the outer loop controller. The level-three controller equation is found so that the robot can follow a desired path described in a Cartesian space. The proposed control system is evaluated in simulation in the MATLAB-SIMULINK environment. It is compared to a PID controller. Simulation results show that the nonlinear adaptive controller has better performances.
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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 | 0.000 |
| 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".