New Kalman Filter Residue-Based Identification and Soft Sensor Design forAccurate Trajectory Tracking with a Fault-tolerant Robot
Bibliographic record
Abstract
A Kalman filter(KF)-based identification, internal model-based controller for accurate tracking a specified trajectory despite the sensor errors, and fault tolerance is proposed.This study was mainly motivated by the need for precision, resolution and accuracy required in robotic applications such as robotic surgery.The computed torque approach is used to map a nonlinear model into a linear one.The sensor errors of the orientation input and the position corrupted by unknown input and output stochastic disturbance and measurement noise.Predictive analytics is used to estimate the true input by exploiting its smoothness and the randomness of the noisy input.The system is described using the Box-Jenkins(BJ) model, which is an augmented model of the true output, termed signal and the disturbance.The BJ model and the associated KF are identified without the a priori knowledge of the statistics of the disturbance and measurement noise.Using the key properties of KF the signal, the output error, the signal model, and the disturbance models, the KF associated with the signal model is accurately identified.An internal model-based state-feedback and feedforward controller is designed to accurately track the desired trajectory.The hardware sensors are replaced by KF-based sensors.The KF ensures fault tolerance.The proposed scheme was successfully evaluated on a physical 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 | 0.001 | 0.002 |
| 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 | 0.001 |
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".