Near-minimum-time control of a robot manipulator
Bibliographic record
Abstract
This thesis deals with the problem of minimum time control of a rigid robot manipulator with point-to-point motion subject to constraints on the control inputs. Due to the nonlinear and coupled dynamics of the robot manipulator, finding minimum time strategies is algorithmically difficult and computationally very intensive, even when the dynamic equations and parameters of the manipulator are precisely known. As a result, the practical applicability of the available methods currently is very limited. In this research, we assume the control inputs are always bang-bang and switch once. Using the Principle of Work and Energy, a simple and practical "zero-net-work" searching approach is proposed. The proposed method focuses on changes in the manipulator's kinetic energy during the time optimal motion, instead of concentrating on the system's state variables, as is usually done in conventional approaches. The "zero-net-work" method is used to develop the controllers for one-link manipulators, a 3-degree of freedom cylindrical manipulator and a two-degree of freedom revolute manipulator. The results show that if the structure of the exact minimum time control is bang-bang with a single switch, using the "zero-net-work" method we will get the exact minimum time solution. If the exact minimum time control has more than one switch, using the "zero-net-work" method we will get a near-minimum-time solution. The major advantages of the proposed method are that it does not require initial boundary value guesses and is computationally efficient.
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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".