Chattering Eliminated and Stable Motion of Biped Robots using a Fuzzy Sliding Mode Controller
Bibliographic record
Abstract
Control of biped walking robots based on designated smooth and stable trajectories is a challenging problem that is the focus of this article. Because of highly nonlinear dynamics of biped robots, minor uncertainties in systems parameters may drastically affect the system performance, leading to chattering phenomenon. To tackle this, a new Sliding Mode Control (SMC) approach is proposed privileging a chattering elimination method based on Fuzzy logic to regulate the switching gain. To this end, first a desired trajectory for the lower body will be designed to alleviate the impacts due to contact with the ground. This is obtained by fitting proper polynomials at appropriate break points. Then, the upper body motion is planned based on the Zero Moment Point (ZMP) criterion to provide a stable motion for the biped robot. Next, dynamics equations will be obtained for both single support phase (SSP) and double support phase (DSP). Finally, the SMC approach is applied for both the SSP and the DSP, while a new chattering elimination method using Fuzzy logic will be proposed based on regulating constant switching gain. Obtained simulation results show that the performance of the system is properly accurate in terms of the tracking errors even in the presence of considerable uncertainties and exerted disturbances. Also, the new proposed method substantially reduces chattering effects and avoids the instability of the biped robot due to this phenomenon, resulting in stable smooth motion control of this complicated system.
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 | 0.000 |
| 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) | 0.001 | 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".