Extended Kalman Filter-Based State Estimation and Adaptive Control of Cable-Driven Parallel Robots
Bibliographic record
Abstract
Cable-Driven Parallel Robots (CDPRs) are used in ever-changing, unstructured, and long-term autonomous operations; however, they require precise component assembly to achieve high positioning accuracy. This article presents an adaptive control framework for CDPRs that addresses actuator position uncertainty in all types of CDPRs without relying on vision-based sensing. The core concept of the developed adaptive control scheme involves employing an Extended Kalman Filter (EKF) to estimate system states, including the uncertain actuator positions and the end-effector pose, and replacing the uncertain parameters in the feedback controller with their estimates. Monte Carlo Simulations (MCSs) are also conducted to evaluate the robustness and stability of the proposed estimation method under the anchor point uncertainties. Moreover, the proposed controller incorporates a robust term to compensate for the unmodeled dynamics and payload changes. The results demonstrate that the adaptive control design effectively reduces the actuator position uncertainty, enhances the end-effector positioning accuracy, and successfully compensates for the payload changes. The performance comparisons of the proposed adaptive controller over its non-adaptive counterpart and PID controller highlight its superior performance in managing the anchor point uncertainties and adapting to the payload changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".