Bioinspired optimization on controlled anthropomorphic manipulator robots
Bibliographic record
Abstract
Abstract Bioinspired optimization algorithms, derived from biological processes such as bacterial foraging and swarm behavior, have shown increasing potential in addressing high-dimensional, nonlinear, and time-varying problems in engineering. Their integration into robotic control architectures enables the development of adaptive, model-flexible schemes that are robust to uncertainty and real-time constraints. Anthropomorphic manipulator robots, widely used in manufacturing and medical applications, require high-performance motion control under structural uncertainty, dynamic perturbations, and limited sensing. This paper proposes a unified and robust control scheme that integrates three key components: (i) a bacterial foraging optimization algorithm for offline initialization of controller weights, (ii) B-spline artificial neural networks for online adjustment of adaptive control gains, and (iii) a robust motion control law based on integral reconstruction theory, which eliminates the need for velocity measurement or full dynamic models and avoids high-gain compensation. This architecture overcomes several limitations of classical model-based, PID, or adaptive-only approaches by combining learning, compensation, and optimization within a scalable framework. The proposed method is validated through multiple simulation studies involving anthropomorphic manipulators with documented physical parameters and subjected to varying disturbances. Comparative analysis demonstrates superior tracking precision, reduced control effort, and faster convergence dynamics. These results confirm the practical viability of the proposed framework for motion control in dynamically uncertain robotic platforms.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".