Intelligent Model Predictive Control of a Flexible-Link Robotic Manipulator
Bibliographic record
Abstract
This paper presents an intelligent, model-based predictive control (IMPC) strategy for motion control a flexible-link robot manipulator. The proposed IMPC is based on a two-level hierarchical control architecture. This control structure is used to combine the advantages of crisp model-based predictive control and knowledge-based soft control techniques. The top-level is a fuzzy-rule based intelligent decision-making system. The low-level consists of two modules: Real time system identification module, and the model-based predictive control (MPC) module. The top-level intelligent fuzzy-rule based tuner interacts with the low-level modules. Based on the desired system performance, the state feedbacks, and the knowledge base, the top-level fuzzy tuner automatically adjusts the tuning parameters of the MPC controller. It is also able to adjust the model structure of system identification module, if necessary, for large model errors, and will increase the robustness of the controller. A multi-stage MPC algorithm is used by MPC module to ensure the nominal stability of the controller based on Lyapunov’s theorem. Physical implementation of the IMPC in a prototype flexible link manipulator system (FLMS) is explored. The performance of the proposed IMPC scheme is evaluated using computer simulations of the prototype FLMS. The results show that the IMPC can effectively control the motion of a flexible-link robot manipulator.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".