Issue Information
Bibliographic record
Abstract
Aims and ScopePapers must include an element of optimization or optimal control and estimation theory to be considered by the journal, and all papers will be expected to include signifi cant novel material.The journal only considers papers that mainly use model based control design methods and hence papers featuring fuzzy control will not be included. Optimal Control Applications and Methodsprovides a forum for papers on the full range of optimal control and related control design methods.The aim is to encourage new developments in optimal control theory and design methodologies that may lead to advances in real control applications.Papers must include an element of optimization for dynamic systems, or optimal control and estimation theory, and all papers will be expected to include significant novel material.The journal focuses on papers that use model-based control design methods.Papers are encouraged on the development of computational algorithms for solving optimal control and dynamic optimization problems.The scope includes papers on optimal estimation and filtering methods that have control-related applications.The journal is also a venue for interesting optimal control applications and design studies.Papers on the theory of optimal systems with engineering applications potential will be particularly welcome including papers on areas such as nonlinear, safety-critical, fault-tolerant, and reliable control.The journal particularly wishes to encourage papers on predictive control dealing with theory and also applications.Such papers should be optimization based, and for linear or nonlinear systems, and can cover topics such as economic model predictive control.Other design methods covered by the journal include H 2 and H ∞ design, linear-quadratic optimal control, nonlinear optimal control, stochastic optimal control, periodic optimal control, optimal filtering and fault estimation, optimal adaptive control, multi-criteria and multiple-model optimal control, singular perturbation methods, repetitive control and switching, optimal control of large-scale or distributed systems, time-delay systems, nonlinear programming and optimization methods, dynamic programming, and static and dynamic optimization techniques.Optimization of dynamic systems, optimal estimation, optimal control, and optimization-based analysis of dynamic systems are within the scope of OCAM.A typical paper would normally involve some form of optimization and some form of dynamic system.A paper that applies an optimization-based soft computing technique (e.g., dynamic neural network) could be appropriate if it includes a rigorous theory for the analysis or design of such systems, or is applied to a novel optimal estimation and control problem.However, soft computing papers that involve only minor changes to existing algorithms, or use only static optimization, are not suitable.There is a strong interest in engineering applications including: automotive systems, aerospace and defence, energy systems (including wind turbines, wave, tidal, battery, fuel cell and solar), marine and, electro-mechanical systems, robotics, power generation and distribution systems, chemical and petrochemical processes, biological and biomedical systems, environmental control, water treatment and distribution, manufacturing and electrical and electronic systems, and networks.Applications of interest include a wide range of interdisciplinary, switching, hybrid, and complex systems problems, where multi-agent solutions, intelligent sensors, and optimization play a role.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.818 | 0.787 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".