Design of controller for robots using the robust root locus of discrete time systems
Bibliographic record
Abstract
A technique for generating multi-parameter root loci of a continuous time feedback system was introduced by Barmish and Tempo (1990). For uncertainties in l parameters of the plant, the robust root locus is reduced to a two-dimensional bounded subset of the complex plane. This is much better than treating the complete l-dimensional set and plotting a large number of ordinary root loci. In this paper, the authors extend this procedure to discrete time systems. A computational method for plotting the robust root locus of discrete time systems is developed. The graph of the locations of the poles of the transfer function of the closed loop system corresponding to each gain can be plotted readily and accurately with this method. The sensitivity of poles to the coefficients of the characteristic polynomial can thus be examined and the optimal tuning gain selected to reach a better robustness. Also a stricter bound of the zeros of the characteristic polynomial is given to further reduce the computation of the robust root locus. The technique is applied to the design of robot manipulators. Simulations of controller design for the PUMA 762 robotic disk grinding process are included.>
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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) | 0.001 | 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 | 0.000 |
| 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".