Disturbance Observer Based on Biologically Inspired Integral Sliding Mode Control for Trajectory Tracking of Mobile Robots
Bibliographic record
Abstract
This paper proposes an integral sliding control system based on the nonlinear disturbance observer, aiming to the trajectory tracking of the mobile robot under the external disturbance. First, a kinematic model of mobile robot was built, besides, the position error signal was gained by the biological membrane potential model, and the problem of velocity oscillation was solved by the design of the backstepping controller. Then, an integral sliding control system was designed in accordance with the kinematic model of the mobile robot, meanwhile, a disturbance observer was designed in consideration of external disturbance to do the real-time observation on the disturbance occurring in the system with an addition of feedforward compensation and the observation error was converged by selecting the design parameters. The Lyapunov function was used to prove the stability of the system. Finally, the simulation of tracking circularity trajectory was utilized, with the comparison of trajectory without the use of jammer, to prove that this method can well overcome the nonlinear and uncertainty originated from external, thereby improving the control performance and increasing the robustness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".