New Fixed-Time Observer-Based Model-Free Fixed-Time Sliding Mode of Joint Angle Commanded NAO Humanoid Robot
Bibliographic record
Abstract
In this article, we present a new fixed-time observer (FTO) with time delay estimation (TDE)-based model-free fixed-time sliding mode for the problem of robust walking of the NAO robot. The proposed technique ensures convergence in fixed time, regardless of initial conditions, thereby enhancing both convergence speed and robustness. This method allows for precise tracking of the joint angles’ positions without depending on the robot’s dynamic models while reducing the chattering via a modified exponential reaching law (MERL). To address the complexities of stabilizing the walking dynamics of the NAO robot, which include highly nonlinear dynamics and limited computational process, the proposed strategy utilizes the TDE technique for system model estimation. To mitigate estimation errors, a novel observer with guaranteed fixed-time stability is proposed. This last helps to enhance the tracking performance. Using the Lyapunov theory and experimental validation, within the proposed composite control method, the proposed nonsingular terminal sliding surface’s fixed-time stability along with the system state’s stability is verified. Significantly improved stability and accuracy in the robot’s joint movements are demonstrated through experimental results, validating the efficacy of the tracking trajectory for robotic systems such as the NAO humanoid robot.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".