Intelligent sliding mode control in flexible structures
Bibliographic record
Abstract
The first objective of this work is to develop an intelligent sliding mode controller for vibration control in flexible structures. The proposed control consists of two processes: system identification and sliding mode control. System identification is performed based on a neural fuzzy (NF) approximator. A novel extended gradient method and a modified least square estimate (LSE) algorithm are proposed for neuro-fuzzy system training. The training is performed in a hybrid approach: the nonlinear parameters in the NF approximator are updated using the extended gradient method while the linear parameters are optimized by the modified LSE. In system control, an enhanced sliding mode (ESM) control system is developed to promote the control effort for active vibration suppression especially in flexible structures. Based on experimental investigation, when the principle of the terminal attractor is used in the classical gradient descent algorithm or sliding mode control systems, it causes implementation problems because the initial condition should be nonzero. The proposed training techniques provide faster convergence while avoiding the associated implementation problems. The stability of the proposed training techniques is demonstrated by the Lyapunov analysis. The effectiveness of the developed techniques is verified experimentally with a flexible structure experimental setup. Test results show that the suggested hybrid training technique can effectively improve the convergence of the NF approximator; the ESM controller can efficiently perform vibration suppression in flexible structures and easy to implement. The commonly used global search method is genetic algorithm (GA). The problems in the classical GA are low convergence speed and lack of fast global search capability for complex search space. The second objective of this work is to develop a more efficient global training approach, called enhanced genetic algorithm (EGA) for system training and optimization applications. Two approaches are proposed: Firstly, a novel group-based branch crossover operator is suggested to thoroughly explore local space and speed up convergence. Secondly, an enhanced MPT (Makinen-Periaux-Toivanen) mutation operator is proposed to promote global search capability for complex search space. The effectiveness of the developed EGA is verified by simulations based on a series of benchmark test problems. Test results show that the branch crossover operator and enhanced MPT mutation operator can effectively improve the convergence speed and global search capability. The EGA technique outperforms other related GA methods with respect to global search efficiency and operation efficiency.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".