A Structured Online Learning Approach to Nonlinear Tracking with Unknown Dynamics
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Bibliographic record
Abstract
One of the most common problems in the control of dynamical systems is to track a desired reference trajectory, which is found in a variety of real-world applications. This chapter extends the Structured Online Learning (SOL) framework to tracking with unknown dynamics. Similar to regulation problems, the applications of tracking control can benefit from Model-based Reinforcement Learning (MBRL) that can handle the parameter updates more efficiently. It proposes an approximate optimal tracking control framework based on a particular structure of nonlinear dynamics, where a linear quadratic discounted cost is assumed. The chapter provides the details of implementation of the obtained framework as a learning-based approach. Initially, the SOL approach was proposed for solving stabilization and regulation problems. The chapter reviews the model-based learning framework. It focuses on the properties of the proposed control scheme rather than the identification process within the simulations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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