Manuscript Topics

Intelligent mechatronics systems (IMS) are growing rapidly under trend, demand, and recent technological developments and this rapidly accelerating interest is observed in different sectors such as manufacturing, energy, transportation, agriculture, health, etc. A common problem in dealing with IMS is to compromise the system output availability and operational cost. In order to solve this tradeoff at system level, implementation of a monitoring system and suitable automation technology is highly needed.

On the other hand, artificial intelligence (AI) and machine learning (ML) are becoming more and more popular though developing technology knowledge. Specially, thanks to learning mechanism such as supervised, unsupervised or reinforcement learning algorithms for health monitoring and control of practical systems, mechatronics systems can potentially become more agile, robust, accurate in terms of dealing with a large data volume. Consequently, development of learning methodologies for condition monitoring, fault diagnosis and prognostics and fault-tolerant control become very essential for the development of intelligent mechatronics systems.

This special issue provides a forum to share most recent developments in the fields of health monitoring, fault diagnosis and advanced learning-based control methodologies for intelligent mechatronics systems. Potential topics include, but are not limited to:

• Learning methodologies for modeling and identification of IMS  
• Advanced control for IMS  
• Learning based fault diagnosis for IMS under feedback  
• Learning based fault prognostics for IMS under feedback  
• Human-machine interface in IMS  
• Fault-tolerant control for IMS  
• Reliability and resilience control for IMS  
• Soft sensor techniques of IMS  
• Condition monitoring and health monitoring of IMS  
• Application studies, ex. Autonomous vehicles, manufacturing, robotics, energy systems.

Instructions for authors
https://www.aimspress.com/mbe/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/