Multiobjective Optimization of an Observer-Based Controller: Theory and Experiments on an Underwater Grinding Robot
Bibliographic record
Abstract
Hydro-Québec operates hundreds of dikes and dams built many decades ago, and is committed to the long-term sustainability of these facilities. The researchers and engineers at its research institute have designed and manufactured a submersible grinding robot prototype capable of performing the important task of grinding underwater metallic structures. This robot, with direct drive linear motors, has an excellent dynamic performance, but lacks intrinsic stiffness. The design of a control system for such a system is a major challenge for control engineers, as the algorithm must achieve sufficient dynamic stiffness to withstand the significant external perturbations generated by the grinding process and the underwater environment, while at the same time minimizing the sensitivity of the control effort to measurement noise. A discrete-time observer-based control structure has already been proposed in a previous work for this purpose, but the empirical design procedure for this structure did not consider the two contradictory objectives. Using the same controller structure, we propose a new design methodology in this brief, based on a multiobjective genetic algorithm, for these mechatronic systems, which are highly prone to the effects of disturbances. The results obtained through optimization are compared with those obtained using the empirical method. The effectiveness of the proposed design is demonstrated through underwater grinding experiments using the robot test bench we have developed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".