An Intelligent Feedforward Controller Utilizing a Modified Gorilla Troops Optimization for Nonlinear Systems
Bibliographic record
Abstract
This paper presents an intelligent feedforward controller based on the feedback linearization approach to control nonlinear systems. In particular, the nonlinear autoregressive moving average (NARMA-L2) network is trained to reproduce the forward dynamics of the controlled system. Consequently, the trained NARMA-L2 network can be immediately integrated into the inverse feedforward control (IFC) structure. In order to improve the NARMA-L2 structure's ability to approximate nonlinear systems, the NARMA-L2 controller is comprised of two wavelet neural networks (WNNs). In addition, the RASP1 function was used as the mother wavelet function in the structure of the WNN rather than the more common Mexican Hat, Gaussian, and Morlet functions. To prevent the limitations of gradient descent (GD) methods, an artificial gorilla troops optimization (GTO) algorithm is used to determine the optimal settings for the NARMA-L2 inverse controller parameters. In particular, a modified version of the GTO algorithm, which is called the Modified GTO (MGTO) algorithm, is proposed in this work for training the NARMA-L2 inverse controller. This algorithm has demonstrated superior optimization outcomes in comparison to other methods. The effectiveness of the proposed control strategy is demonstrated using two nonlinear dynamical systems. Specifically, several evaluation tests are used to assess the effectiveness of the WNN-based NARMA-L2 in terms of control accuracy and robustness against external disturbances in each of the systems under consideration. These tests clearly demonstrated the effectiveness of the control system. Finally, a comparison study showed that the proposed WNN-based NARMA-L2 controller achieved better control results compared to the multilayer perceptron (MLP) and the radial basis function (RBF)-based NARMA-L2 controllers.
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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) | 0.001 | 0.001 |
| 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) | 0.002 | 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".