Saturday, 13 May 2017

CFP: IEEE TNNLS Special Issue on Intelligent Control through Neural Learning and Optimization for Human Machine Hybrid Systems

During the recent decades, there are a vast number of learning methods for designing intelligent controllers for human machine hybrid systems. However, a further consideration not only is guaranteeing the control stability, but is optimality based on a predefined cost function to determine the performance of the human machine hybrid systems. Therefore, we are faced with a need for improved control schemes, which not only achieve the stability of the human machine hybrid systems, but also keep the cost of the systems as small as possible. The special issue addresses a broad spectrum of topics ranging from deterministic and stochastic intelligent control design for various human machine hybrid systems such as unmanned aerial vehicles, intelligent quadruped robots, industrial robots, robotic exoskeletons, biped robots, wheeled balance transporters, to the optimization of the learning algorithm. Special attention should be given to how to optimize controller design, achieve the high accurate performance for the human machine interactions, and
handle nonlinearities and the unknown system dynamics. This includes modeling, learning control, neural network adaptations, iterative learning, deep learning, reinforcement learning, dynamic programming and testing the effectiveness of the controllers. The special issue publishes original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications to the field of intelligent control for human machine hybrid systems.

Topics explored in this special issue include, but are not limited to:

• Learning and Optimizations for the intelligent control;
• Adaptive dynamic programming for human machine hybrid systems and their applications;
• Iterative learning control for human machine hybrid systems and their applications;
• Deep Learning for human machine interactions;
• Reinforcement learning to handle nonlinearities for human machine hybrid systems;
• Learning control design for intelligent robots;
• High accurate tracking control via learning for multi-robot systems and applications;
• Modeling and learning control for humanoid robots and applications;
• Identification and learning control design for quadruped robots;
• System design and learning control for industrial robots;
• Learning control and optimizations for exoskeletons;
• Learning control and balance analysis for wheeled balance transporters;
• Learning control and optimizations for aerial vehicles;
• Learning control and stability analysis for humanoid robots or quadruped robots;
• Modeling, identification and optimizations via learning;
• Neural network control and practical applications in model-free environment;
• New applications of learning control for human machine hybrid systems.


November 15, 2017 – Deadline for manuscript submissions
February 15, 2018 – Information about manuscript acceptance
April 15, 2018 – Submission of revised paper
June 15, 2018 – Final decisions
October 15, 2018 – Estimated publication date

Wei He, School of Automation and Electrical Engineering, University of Science and Technology Beijing, China
Changyin Sun, School of Automation, Southeast University, Nanjing, China
Donald C. Wunsch, Department of Electrical & Computer Engineer, Missouri University of Science & Technology, US
Richard Yi Da Xu, School of Computing and Communications, University of Technology Sydney, Australia


  1. Read the information for Authors at
  2. Submit your manuscript at the TNNLS webpage ( and follow the submission procedure. Please, clearly indicate on the first page of the manuscript and in the cover letter that the manuscript is submitted to the special issue on Intelligent Control through Neural Learning and Optimization for Human Machine Hybrid Systems. Send an email to the leading editor Prof. Wei He ( with subject TNNLS special issue submission to notify about your submission.
  3. Early submissions are welcome. We will start the review process as soon as we receive your contributions.

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