Neural networks-based reinforcement learning control of autonomous systems (NRLC-AS)

Neural networks-based reinforcement learning control (NRLC) of autonomous systems is an active field due to its theoretical challenges and crucial applications. Note that there exist numerous difficulties in enhancing the intelligence and reliability of autonomous systems since autonomous and reliable techniques of guidance, navigation and control functionals are extremely involved in face of sophisticated and hazardous environments. In this context, high-intelligence reliable control technologies, especially based on neural networks tools, of autonomous systems are persistently pursued in trajectory tracking, path following, waypoints guidance, cooperative formation, etc. In addition, massive nonlinearities, sensor fault diagnosis, actuator failures tolerance, environment abnormalities and civil requirements have led to strong demands for the NRLC technologies in autonomous systems. Reinforcement learning, inspired by learning mechanisms observed in mammals, is concerned with how agent and actor ought to take actions to optimize a cost of its long-term interactions with the environment, and is gradually becoming the focus of learning control for autonomous systems. The autonomous systems inevitably suffer from actuator faults, component failures, insecurity factors, complex uncertainties, such that neural networks induced intelligence in autonomous control, fault tolerant control, network communication and signal progressing becomes dramatically significant. To be specific, by combining with neural networks and reinforcement learning, advances in the NRLC technologies of autonomous systems are exclusively pursued in this special issue.

This special issue will feature the mostly recent developments and the state-of-the-art of NRLC techniques for autonomous systems including ground, marine, and mobile vehicles, etc. The target audience includes both academic researchers and industrial practitioners. It aims to provide a platform for sharing recent results and team experience in intelligent learning control of autonomous systems. Topics to be covered in this special issue include, but are not limited to, the following

• Neural networks-based reinforcement learning control of multiple autonomous systems;
• Neural networks optimization-based reliable control of autonomous systems under multiple operating conditions;
• Neural networks-based health monitoring and supervisory reliable control of autonomous systems;
• Neural networks-based fault diagnosis and prognostics of autonomous systems
• Neural networks-based intelligence application in resilient autonomous systems;
• Neural networks learning-based location and navigation of autonomous systems;
• Neural networks learning-based decision making of autonomous systems;
• Neural networks learning for perception and recognition of autonomous systems;
• Neural networks-based resilience control of autonomous systems;
• Neural networks-based learning control application studies such as ground/mobile vehicles/marine vehicles.

Submission Guideline:

The website link of Neurocomputing is https://www.sciencedirect.com/journal/neurocomputing and before submission, authors should carefully read over the journal’s Author Guidelines, which are located at https://www.elsevier.com/journals/neurocomputing/0925-2312/guide-for-authors. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at https://ees.elsevier.com/neucom/default.asp

When submitting papers, please make sure choose the Article Type “NRLC of Autonomous system“.

The corresponding author will have to create a user profile if one has not been established before at the Editorial Manager.

Important Dates :

Paper submission deadline: 30 September, 2020

Review comments to authors: 30 November, 2020

Revision submission deadline: 30 January, 2021

Final decisions to authors: 30 March, 2021

Publication date: June, 2021

Guest Editors:

Hamid Reza Karimi, Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy, e-mail: hamidreza.karimi@polimi.it

Ning Wang, College of Shipbuilding Engineering, Harbin Engineering University, Harbin, P.R. China, e-mail: n.wang@ieee.org

Xu Jin, Department of Mechanical Engineering, Kentucky University, Lexington, KY, United States, e-mail: xu.jin@uky.edu

Ali Zemouche, Lorraine University – CRAN UMR 7039, France, email: ali.zemouche@univ-lorraine.fr