Neural-based control and stability analysis of a class of nonlinear systems: Base-excited inverted pendulums
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Bibliographic record
Abstract
This paper presents a novel application of multilayer neural networks for online control of a class of base-excited inverted pendulums. The pendulum has two degrees of rotational freedom and its base-point moves freely in three-dimensional space. The goal is to apply control torques to keep the pendulum in a desired orientation, in spite of disturbing base-point movement. Four three-layered neural networks are trained online to represent the inverse dynamics of the plant within a controller. The conditions of training accuracy, to guarantee the stability of such a non-autonomous closed-loop system, are established using Lyapunov stability theory. The proposed neural controller is examined through simulations. Its performance is also compared with the performance of a Lyapunov controller from the most recent published work. It is shown that the proposed control scheme is simple in implementation in the sense that it does not require a mathematical model of the target pendulum or the measurement of the base-point movement. At the same time, it produces fast, yet well-damped responses with smooth control torques. The work presented here can benefit practical problems such as the study of stable locomotion of the human upper-body and bipedal robots.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it