Special Issue "Monitoring and Self-Learning Control of Water Systems"
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".
Deadline for manuscript submissions: closed (10 January 2023) | Viewed by 1306
Special Issue Editors
Interests: water resource management; water-energy-food nexus; reinforcement learning; autonomous agricultural production; multi-agent agricultural robotic systems
Interests: planning and control of autonomous systems; supervision and advanced control of processes and systems; control of large scale systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The control of water systems (e.g., water distribution, irrigation systems) has become an important research topic because of the significance of water for human beings. The optimization of water systems involves complex mathematical equations for hydraulic and hydrology processes, multiple-input/multiple-output characteristics, as well as the possible, additive of parametric uncertainties. Therefore, identify the systems, model the dynamics, diagnose functionality to improve system performance (e.g., optimal water usage, efficient operational cost) is crucially important and challenging.
Due to the development of sensing and communication techniques, a vast amount of data are available which leads to groundbreaking advances in both system identification and automatic control. Self-learning control is an advanced control method which can update current control model through incorporating new data during iterative process under specific objectives. Self-learning control includes but not limited to reinforcement learning, model predictive control (MPC), and data-based MPC. The learning ability and reconfiguration strategy make self-learning control be able to operate water systems autonomously with accuracy and robustness.
This Special Issue aims to gather innovative technologies, methods and practices related to system monitoring (sensing systems of IoT, cloud computing and proximal/remote sensors) and optimal control (RL, MPC, data-based, learning-based) of water systems to overcome the challenges of global water scarcity and climate change. Research and review articles, theoretical or practical works will all be considered as valuable contributions to this Special Issue.
Dr. Congcong Sun
Prof. Dr. Vicenç Puig
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- water distribution system
- smart irrigation system
- digital technologies
- remote and proximal sensing
- MPC
- reinforcement learning
- data-based
- system identification
- optimization and automatic control
