Distributed fault-tolerant environments monitoring systems for aircraft cabins

Rui Wang

Multiagent Systems: Decentralized, Distributed and Cloud-Based Control Strategies

Magdi S. Mahmoud

An integral ingredient to the operation of industrial or engineering systems, including cooperative robotics, sensor networks, and grid computing, is its control architecture consisting of hardware and software protocols for exchanging system status and control signals. In conventional systems, this is accomplished by Supervisory Control and Data Acquisition (SCADA) systems. Current trends to control and monitor the operation of industrial or engineering systems are however moving toward the use of an automated agent technology, which is generally known as a multiagent system (MAS). A multiagent system is a combination of several agents working in collaboration pursuing assigned tasks to achieve the overall goal of the system. The MAS has become an increasingly powerful tool in developing complex systems that take advantages of agent properties and it is autonomous in that they operate without human interventions.

In this presentation, we focus control strategies pertaining to MAS. Specifically, we address three classes of strategies:

• Distributed Strategies in which the agent (local) control is equipped with additional information from neighbors (agent-to-agent communication) to achieve
  the global motion-coordination task.
  
  
• Decentralized Strategies in which of the agent (local) controls are independent but collaborate in harmony with the aid of a central coordinator to attain the overall cited goal.
  
  
• Cloud-based Strategies in which the autonomous agents are required to achieve a common coordination objective by exchanging data over a shared cloud registry. The registry is accessed asynchronously by different agents, and direct inter-agent communication is not possible. 

Our main goal is illuminate the merits/demerits of the foregoing strategies as well as the potential application of each. In addition, we discuss several possible extensions for industrial multiagent control systems.

Extended Kalman Filters for Estimation Problems in Industrial Applications

Paolo Mercorelli

Kalman’s optimum linear filter has proved to be very popular. Nevertheless, very often it is not always possible to use a linear system in practical applications. Extended Kalman Filters (EKFs) are used in different fields of industrial applications to reduce the number of sensors and simultaneously to improve the quality of the signals available from the process. EKFs are numerical efficient structures applied through microcontrollers in real industrial applications. Therefore, EKF approach appears to be one of the most suitable ones for problem estimation. The strategic choice of an EKF approach can be justified if the deterministic part of the model can be considered “dominant” and thus in this context, a first order linearized approach, which the EKF is based on, can be taken into consideration for possible industrial applications. After a short overview on the fundamental aspects of the background of EKF, this presentation intends to show how EKFs can be applied as an estimator in different fields of applications such as, for instance, in Thermal Systems, Flexible Actuators, Electromagnetic Actuators, Lithium-Ion Battery as well as in Permanent Magnet Synchronous Motors and other systems. Each System is analyzed in accordance with its physical inside, its intrinsic challenges and dedicated EKFs are proposed to solve specific issues of the considered real application. Measured results related to each application are shown and discussed.

• Machine Learning and Artificial Intelligence
• Systems Biology and Life Systems
• Sensor Networks and Internet of Things
• Big Data and Cloud Computing
• Discrete Event and Hybrid Systems
• Fault Diagnosis and Fault Tolerance
• Instrumentation and Control Techniques
• Load Modeling and Forecasting
• Mechatronics Systems
• Model Calibration and Validation
• System Reliability, Security and Adequacy
• Smart Grids and Distributed Generation Systems
• Chaos, Fractals and Applications
• Robotic Systems
• Modeling & Control of Renewable Energy Systems
• Modeling & Control of Power Systems
• Modeling of Physiological & Biomedical Systems
• Control in Education

Submission and Publications

Papers should describe original and unpublished work on the above or the related topics. Instructions for electronic submission and paper template (LaTeX or Word) are available on ICMIC2020 website. All papers must STRICTLY follow the formatting instructions as provided at Template (Word Template and Latex Template). All conference papers will be published by a book or proceedings then indexed by EI Compendex. Some selected papers by an objective peer review process will be recommended as special issues to IJMIC (https://www.inderscience.com/ijmic) and IJCAT (https://www.inderscience.com/ijcat).

Submission Website

https://easychair.org/conferences/?conf=icmic2020