Control of a modified double inverted pendulum using machine learningbased model predictive control
Bibliographic record
Abstract
Abstract: A machine learning-based controller (MLC) has been developed for a modified double inverted pendulum on a cart (MDIPC). First, the governing differential equations of the system are derived using the Lagrangian method. Then, a dataset is generated to train and test the machine learning-based models of the plant. Different types of machine learning models such as artificial neural networks (ANN), deep neural networks (DNN), long-short-term memory neural networks (LSTM), gated recurrent unit (GRU), and recurrent neural networks (RNN) are employed to capture the system’s dynamics. DNN and LSTM are selected due to their superior performance compared to other models. Finally, different variations of the Model Predictive Controller (MPC) are designed, and their performance is evaluated in terms of running time and tracking error. The proposed control methods are shown to have an advantage over the conventional nonlinear and linear model predictive control methods in simulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".