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Learning Systems Based Control and Optimization of Complex Nonlinear Systems

Submission Deadline: 30 November 2017

Submission Deadline: 30 November 2017

IEEE Access invites manuscript submissions in the area of Learning Systems Based Control and Optimization of Complex Nonlinear Systems.

Recent years have witnessed the growing interests in complex nonlinear systems from both academic and engineering communities since most practical systems, such as wind energy systems or robot manipulators, are inherently nonlinear. Due to their inherent approximation capabilities, learning systems (such as neural network, fuzzy logic, support vector machine, etc.) have been found to be particularly useful for the control and optimization of modern complex nonlinear dynamic systems. The learning system is envisioned as an effective tool to enhance the system performance of complex systems. Past pioneer approaches were developed for multifarious uncertain nonlinear systems such as the strict-feedback systems and the pure-feedback systems in discrete-time or continuous-time forms. The different controllers have been framed using various techniques, e.g., feedback linearization, inversion control, backstepping design, fuzzy control and neural control. Apart from the work focused on the stability of the control system, the optimal solution of the control system has also received a great deal of attention. Although much effort has been made to the control and optimization of complex nonlinear systems, effective systematic methods for this research field are still a challenging topic, especially for the potential of learning based control and optimization design in engineering applications. The primary objective of this Special Section in IEEE Access is dedicated to the learning systems based control and optimization of complex nonlinear systems and to highlight the latest advances in this field. We invite high quality, original research

The primary objective of this Special Section in IEEE Access is dedicated to the learning systems based control and optimization of complex nonlinear systems and to highlight the latest advances in this field. We invite high quality, original research articles, as well as review articles, focused on complex nonlinear system theory with new applications.

Potential topics include but are not limited to the following:

  • Learning systems (e.g., neural network, fuzzy logic) based control of strict-feedback systems, pure feedback systems, switching systems, stochastic systems, and so on
  • Learning systems based fault detection and control in complex nonlinear systems
  • Learning systems based nonlinear multi-agent systems
  • Learning systems based control of complex nonlinear systems with various constraints such as state constraint, input constraint, and output constraint
  • Learning systems based optimal control of complex nonlinear systems in various forms, e.g., adaptive dynamic programming (ADP) for nonlinear systems
  • Learning systems based optimal control of complex nonlinear systems with various constraints
  • Learning systems based optimal control of nonlinear multi-agent systems
  • Learning systems based methods in practical applications such as wind energy systems, solar systems, tele-operation systems, PWM Rectifiers, and robots

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor: Huanqing Wang, Carleton University, Canada

Guest Editors:

  1. Hamid Reza Karimi, Politecnico di Milano, Italy
  2. Wenchao Meng, Carleton University, Canada
  3. Yongming Li, City University of Hong Kong, Hong Kong
  4. Dawei Shi, Harvard University, USA

 

Related IEEE Access Special Sections:

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  2. System-Level Design Automation Methods for Multi-Processor System-on-Chips
  3. Recent Advances in Computational Intelligence Paradigms for Security and Privacy for Fog and Mobile Edge Computing

 

IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://mc.manuscriptcentral.com/ieee-access

For inquiries regarding this Special Section, please contact: Bora M. Onat, Managing Editor, IEEE Access (Phone: (732) 562-6036, specialsections@ieee.org)