A backstepping approach for the design of a nonlinear controller for a two-wheeled autonomous vehicle
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Bibliographic record
Abstract
This paper proposes an innovative approach for the design of a nonlinear controller to stabilize an autonomous mobile robot. The design approach combines a nonlinear control design method with a root-finding algorithm for nonlinear algebraic equations. For the design, the robot model is divided into two parts: a state space model with intermediate control inputs and algebraic nonlinear equations relating the true and the intermediate control inputs. First, a suitable change of variable is applied to the traditional robot dynamics to reveal the strict feedback structure of this state space model. Next, a three-step backstepping control design method is applied to obtain the intermediate control input expressions. Finally, the true control inputs are found by solving iteratively the nonlinear equations that relates intermediate and true control inputs. The proposed design strategy is tested in simulation. The results show that good tracking performances are achieved.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it