Example of Optimal Control Applications and Methods format
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Optimal Control Applications and Methods — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Applied Mathematics #137 of 548 down down by 40 ranks
Control and Optimization #37 of 111 down down by 18 ranks
Control and Systems Engineering #105 of 260 down down by 25 ranks
Software #202 of 389 down down by 38 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 388 Published Papers | 1265 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 04/08/2023
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Related Journals

open access Open Access
recommended Recommended

Taylor and Francis

Quality:  
High
CiteRatio: 6.8
SJR: 1.321
SNIP: 1.764
open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 6.6
SJR: 1.402
SNIP: 1.71
open access Open Access

Taylor and Francis

Quality:  
High
CiteRatio: 4.6
SJR: 0.601
SNIP: 1.294

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.252

14% from 2018

Impact factor for Optimal Control Applications and Methods from 2016 - 2019
Year Value
2019 1.252
2018 1.452
2017 1.614
2016 1.558
graph view Graph view
table view Table view

3.3

10% from 2019

CiteRatio for Optimal Control Applications and Methods from 2016 - 2020
Year Value
2020 3.3
2019 3.0
2018 2.9
2017 3.0
2016 2.8
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 14% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 10% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.458

0% from 2019

SJR for Optimal Control Applications and Methods from 2016 - 2020
Year Value
2020 0.458
2019 0.457
2018 0.507
2017 0.825
2016 0.48
graph view Graph view
table view Table view

1.076

36% from 2019

SNIP for Optimal Control Applications and Methods from 2016 - 2020
Year Value
2020 1.076
2019 0.791
2018 1.056
2017 1.235
2016 0.89
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 0% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 36% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
Optimal Control Applications and Methods

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Wiley

Optimal Control Applications and Methods

Optimal Control Applications & Methods provides a forum for papers on the full range of optimal and optimization based control theory and related control design methods. The aim is to encourage new developments in control theory and design methodologies that will lead to real ...... Read More

Mathematics

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Last updated on
04 Aug 2023
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ISSN
0143-2087
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Impact Factor
High - 1.301
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
apa
i
Citation Type
Numbered
[25]
i
Bibliography Example
Beenakker, C.W.J. (2006) Specular andreev reflection in graphene.Phys. Rev. Lett., 97 (6), 067 007. URL 10.1103/PhysRevLett.97.067007.

Top papers written in this journal

Journal Article DOI: 10.1002/OCA.939
ACADO toolkit—An open-source framework for automatic control and dynamic optimization
Boris Houska1, Hans Joachim Ferreau1, Moritz Diehl1

Abstract:

In this paper the software environment and algorithm collection ACADO Toolkit is presented, which implements tools for automatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct optimal control, including model predictive control as well as state and paramete... In this paper the software environment and algorithm collection ACADO Toolkit is presented, which implements tools for automatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct optimal control, including model predictive control as well as state and parameter estimation. The ACADO Toolkit is implemented as a self-contained C++ code, while the object-oriented design allows for convenient coupling of existing optimization packages and for extending it with user-written optimization routines. We discuss details of the software design of the ACADO Toolkit 1.0 and describe its main software modules. Along with that we highlight a couple of algorithmic features, in particular its functionality to handle symbolic expressions. The user-friendly syntax of the ACADO Toolkit to set up optimization problems is illustrated with two tutorial examples: an optimal control and a parameter estimation problem. Copyright © 2010 John Wiley & Sons, Ltd. read more read less

Topics:

Software design (52%)52% related to the paper, Optimization problem (52%)52% related to the paper, Automatic control (51%)51% related to the paper
958 Citations
open accessOpen access Journal Article DOI: 10.1002/OCA.2123
Optimization‐based autonomous racing of 1:43 scale RC cars
Alexander Liniger1, Alexander Domahidi1, Manfred Morari1

Abstract:

Summary This paper describes autonomous racing of RC race cars based on mathematical optimization. Using a dynamical model of the vehicle, control inputs are computed by receding horizon based controllers, where the objective is to maximize progress on the track subject to the requirement of staying on the track and avoidi... Summary This paper describes autonomous racing of RC race cars based on mathematical optimization. Using a dynamical model of the vehicle, control inputs are computed by receding horizon based controllers, where the objective is to maximize progress on the track subject to the requirement of staying on the track and avoiding opponents. Two different control formulations are presented. The first controller employs a two-level structure, consisting of a path planner and a nonlinear model predictive controller (NMPC) for tracking. The second controller combines both tasks in one nonlinear optimization problem (NLP) following the ideas of contouring control. Linear time varying models obtained by linearization are used to build local approximations of the control NLPs in the form of convex quadratic programs (QPs) at each sampling time. The resulting QPs have a typical MPC structure and can be solved in the range of milliseconds by recent structure exploiting solvers, which is key to the real-time feasibility of the overall control scheme. Obstacle avoidance is incorporated by means of a high-level corridor planner based on dynamic programming, which generates convex constraints for the controllers according to the current position of opponents and the track layout. The control performance is investigated experimentally using 1:43 scale RC race cars, driven at speeds of more than 3 m/s and in operating regions with saturated rear tire forces (drifting). The algorithms run at 50 Hz sampling rate on embedded computing platforms, demonstrating the real-time feasibility and high performance of optimization-based approaches for autonomous racing. Copyright © 2014 John Wiley & Sons, Ltd. read more read less

Topics:

Control theory (58%)58% related to the paper, Obstacle avoidance (54%)54% related to the paper, Linearization (53%)53% related to the paper, Dynamic programming (51%)51% related to the paper
View PDF
423 Citations
Journal Article DOI: 10.1002/OCA.957
An hp‐adaptive pseudospectral method for solving optimal control problems
Christopher L. Darby1, William W. Hager1, Anil V. Rao1

Abstract:

SUMMARY An hp-adaptive pseudospectral method is presented for numerically solving optimal control problems The method presented in this paper iteratively determines the number of segments, the width of each segment, and the polynomial degree required in each segment in order to obtain a solution to a userspecified accuracy St... SUMMARY An hp-adaptive pseudospectral method is presented for numerically solving optimal control problems The method presented in this paper iteratively determines the number of segments, the width of each segment, and the polynomial degree required in each segment in order to obtain a solution to a userspecified accuracy Starting with a global pseudospectral approximation for the state, on each iteration the method determines locations for the segment breaks and the polynomial degree in each segment for use on the next iteration The number of segments and the degree of the polynomial on each segment continue to be updated until a user-specified tolerance is met The terminology ‘hp’ is used because the segment widths (denoted h) and the polynomial degree (denoted p) in each segment are determined simultaneously It is found that the method developed in this paper leads to higher accuracy solutions with less computational effort and memory than is required in a global pseudospectral method Consequently, the method makes it possible to solve complex optimal control problems using pseudospectral methods in cases where a global pseudospectral method would be computationally intractable Finally, the utility of the method is demonstrated on a variety of problems of varying complexity Copyright 2010 John Wiley & Sons, Ltd read more read less

Topics:

Pseudospectral optimal control (75%)75% related to the paper, Chebyshev pseudospectral method (67%)67% related to the paper, Legendre pseudospectral method (67%)67% related to the paper, Gauss pseudospectral method (67%)67% related to the paper, Degree of a polynomial (56%)56% related to the paper
View PDF
388 Citations
Journal Article DOI: 10.1002/OCA.710
Optimal control of an HIV immunology model
Hem Raj Joshi1

Abstract:

A system of ordinary differential equations, which describes the interaction of HIV and T-cells in the immune system is utilized, and optimal controls representing drug treatment strategies of this model are explored. Two types of treatments are used, and existence and uniqueness results for the optimal control pair are estab... A system of ordinary differential equations, which describes the interaction of HIV and T-cells in the immune system is utilized, and optimal controls representing drug treatment strategies of this model are explored. Two types of treatments are used, and existence and uniqueness results for the optimal control pair are established. The optimality system is derived and then solved numerically using an iterative method with a Runge–Kutta fourth order scheme. Copyright © 2002 John Wiley & Sons, Ltd. read more read less

Topics:

Optimal control (57%)57% related to the paper, Iterative method (54%)54% related to the paper, Ordinary differential equation (52%)52% related to the paper, Uniqueness (50%)50% related to the paper
349 Citations
Journal Article DOI: 10.1002/OCA.727
Simulation of Pedestrian Flows by Optimal Control and Differential Games
Serge P. Hoogendoorn1, Piet H. L. Bovy1

Abstract:

Gaining insights into pedestrian flow operations and assessment tools for pedestrian walking speeds and comfort is important in, for instance, planning and geometric design of infrastructural facilities, as well as for management of pedestrian flows under regular and safety-critical circumstances. Pedestrian flow operations a... Gaining insights into pedestrian flow operations and assessment tools for pedestrian walking speeds and comfort is important in, for instance, planning and geometric design of infrastructural facilities, as well as for management of pedestrian flows under regular and safety-critical circumstances. Pedestrian flow operations are complex, and vehicular flow simulation modelling approaches are generally not applicable to pedestrian flow modelling. This article focusses on pedestrian walking behaviour theory and modelling. It is assumed that pedestrians are autonomous predictive controllers that minimize the subjective predicted cost of walking. Pedestrians predict the behaviour of other pedestrians based on their observations of the current state as well as predictions of the future state, given the assumed walking strategy of other pedestrians in their direct neighbourhood. As such, walking can be represented by a (non-co-operative or co-operative) differential game, where pedestrians may or may not be aware of the walking strategy of the other pedestrians. Copyright © 2003 John Wiley & Sons, Ltd. read more read less

Topics:

Pedestrian (58%)58% related to the paper
287 Citations
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13. What is Sherpa RoMEO Archiving Policy for Optimal Control Applications and Methods?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Optimal Control Applications and Methods. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Optimal Control Applications and Methods?

The 5 most common citation types in order of usage for Optimal Control Applications and Methods are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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