Advances in Autonomous Control Systems and Their Applications: 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (15 November 2024) | Viewed by 1818

Special Issue Editors


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Dept. of Electronic Technology, Systems Engineering and Automatic Control, University of Cantabria, E.T.S. de Náutica, C/ Gamazo 1, 39004 Santander, Spain
Interests: system identification; guidance navigation and control of marine vehicles; dynamic positioning of marine structures; marine remote laboratory
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Guest Editor
School of Engineering, University of Portsmouth, Anglesea Building, Anglesea Road, Portsmouth, PO1 3DJ, UK
Interests: computational optimal control; nonlinear control; fault diagnosis; fault-tolerant control; autonomous control systems; state estimation; smart grids; solar energy; control of power systems; control of energy storage
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electronics Technology, Systems and Automation Engineering, Universidad de Cantabria, 39004 Santander, Spain
Interests: guidance navigation and control of marine vehicles; maritime unmanned systems; engineering education; control systems engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous control refers to the ability of a system to function well in the face of high environmental uncertainty, as well as the ability to correct system faults without the need for external intervention. This is distinct from automation, which is commonly characterized as an operation carried out with little or no human intervention.

While it is common to think of different levels of autonomy, it is acknowledged that the availability of inexpensive sensors, the ability to handle massive volumes of data, and the processing capacity and methodologies to execute essential decision-making algorithms in real time are key factors in making real autonomous control systems possible.

Over the past few decades, significant advances have been made in control systems for different types of autonomous and semi-autonomous vehicles in both military and civilian domains. In most of these applications, the control system plays a role of paramount importance in achieving the required performance, safety, and autonomy specifications. This Special Issue focusses on emerging control, sensing, dynamic modelling, and system identification methodologies and applications related to autonomous and semi-autonomous maritime, aerial, and terrestrial vehicles both in military and civilian domains.

We aim to seek high-quality submissions reporting research and real applications covering topics including, but not limited to, the following:

  • Advanced control methodologies for autonomous vehicles;
  • Sensing for autonomous vehicles;
  • Modelling and system identification for the autonomous control of vehicles;
  • Applications to autonomous and semi-autonomous under-water vessels, surface vessels, aerial vehicles, and ground vehicles.

Dr. Elías Herrero
Prof. Dr. Victor Becerra
Dr. Francisco Jesus Velasco
Guest Editors

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Keywords

  • autonomous control
  • autonomous vehicles
  • sensing for autonomy
  • modelling and system identification for autonomous control
  • fault-tolerant control for autonomous vehicles

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Published Papers (1 paper)

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Research

23 pages, 8203 KiB  
Article
Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs
by Federico Pacini, Pierpaolo Dini and Luca Fanucci
Electronics 2024, 13(8), 1507; https://doi.org/10.3390/electronics13081507 - 16 Apr 2024
Cited by 3 | Viewed by 1170
Abstract
Driving a motorized wheelchair is not without risk and requires high cognitive effort to obtain good environmental perception. Therefore, people with severe disabilities are at risk, potentially lowering their social engagement, and thus, affecting their overall well-being. Therefore, we designed a cooperative driving [...] Read more.
Driving a motorized wheelchair is not without risk and requires high cognitive effort to obtain good environmental perception. Therefore, people with severe disabilities are at risk, potentially lowering their social engagement, and thus, affecting their overall well-being. Therefore, we designed a cooperative driving system for obstacle avoidance based on a trained reinforcement learning (RL) algorithm. The system takes the desired direction and speed from the user via a joystick and the obstacle distribution from a LiDAR placed in front of the wheelchair. Considering both inputs, the system outputs a pair of forward and rotational speeds that ensure obstacle avoidance while being as close as possible to the user commands. We validated it through simulations and compared it with a vector field histogram (VFH). The preliminary results show that the RL algorithm does not disruptively alter the user intention, reduces the number of collisions, and provides better door passages than a VFH; furthermore, it can be integrated on an embedded device. However, it still suffers from higher jerkiness. Full article
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