International Journal of Control Science and Engineering

International Journal of Control Science and Engineering seeks to provide an outlet for technical papers on advances in the field of control systems and control technology. It aims at speedy, online publication of original, peer-reviewed papers in all established and newly emerging areas of control theory and applications, encompassing modeling, identification, estimation, analysis, design, implementation of control systems, and in broader and related areas of signal processing and systems and information sciences. The Journal employs a paperless, electronic submission and evaluation system to promote a rapid turnaround in the peer review process.


ICV 2015: 68.28; ICV 2016: 85.70
Editor-in-chief: Artur Opalinski
p-ISSN: 2168-4952
e-ISSN: 2168-4960
Website: http://journal.sapub.org/control




International Journal of Control Science and Engineering seeks to provide an outlet for technical papers on advances in the field of control systems and control technology. It aims at speedy, online publication of original, peer-reviewed papers in all established and newly emerging areas of control theory and applications, encompassing modeling, identification, estimation, analysis, design, implementation of control systems, and in broader and related areas of signal processing and systems and information sciences. The Journal employs a paperless, electronic submission and evaluation system to promote a rapid turnaround in the peer review process.
Subject areas suitable for publication include, but are not limited to the following fields:
  Adaptive Control
  Complex Systems
  Control Theory and Systems
  Cooperative Control
  Decentralized Control
  Digital Control
  Discrete Event Systems
  Distributed Parameter Systems
  Dynamical Systems and Scientific Computing
  Fuzzy Control
  Fuzzy Neural Systems
  Hybrid Control
  Intelligent Control
  Large Scale Systems
  Learning Control
  Linear Control
  Multi-agent Systems
  Multivariable Systems
  Mathematical Modeling
  Nonlinear Control
  Nonlinear Systems Optimal Control
  Power Systems
  Predictive Control
  Process Control
  Sampled-data Control
  Stochastic Control