A Set-Theoretic Control Approach to the Trajectory Tracking Problem for Input–Output Linearized Wheeled Mobile Robots
Bibliographic record
Abstract
This paper proposes a set-theoretic receding horizon control scheme to address the trajectory tracking problem for input-constrained differential-drive robots. The proposed solution is derived starting from an input-output linearized description of the robot kinematics and a worst-case characterization of the orientation-dependent input constraint acting on the feedback linearized model. In particular, offline, given a worst-case characterization of the constraint set, we analytically design the smallest robust control invariant region for the tracking error. Moreover, such a region is recursively enlarged by computing a family of robust one-step controllable sets whose union characterizes the controller’s domain of attraction. Online, such sets and the knowledge of the current robot’s orientation are leveraged to define a non-conservative control law ensuring bounded tracking error. The effectiveness of the proposed strategy is experimentally validated using a Khepera IV robot, and its performance is contrasted with four alternative trajectory tracking algorithms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".