Neural-based control and stability analysis of a class of nonlinear systems: Base-excited inverted pendulums
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".