Machine Learning in Model Predictive Control and Optimal Control

Dear Colleagues,

Machine learning (ML) creates new paradigms and opportunities in the design of advanced process control systems for chemical processes. While machine learning techniques such as neural networks and reinforcement learning have been successfully applied to model predictive control (MPC) and optimal control schemes, a variety of theoretical issues, such as stability, robustness, and optimality, as well as some practical challenges such as data limitation and computational efficiency need to be addressed. This Special Issue intends to provide a platform for researchers and practitioners to share state-of-the-art algorithms and methods for both theory and application works to address some of the aforementioned fundamental challenges associated with using machine learning in MPC and optimal control.

Potential topics include, but are not limited to:

• Novel ML methods for model development and theoretical analysis on the generalization performance of ML models;
• Theoretical methodologies and applications of predictive control and optimal control using machine learning techniques;
• ML in parameter and state estimation, fault detection, soft sensing, and their applications in MPC and optimal control;
• Computational development of ML-based MPC and optimal control systems to address practical challenges such as computational efficiency, feasibility for large-scale systems, etc.

Dr. Zhe Wu
Dr. Xunyuan Yin
Dr. Yan Qin
Prof. Dr. Xiaodong Xu
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 250 words) can be sent to the Editorial Office for assessment.

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. Processes 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 2400 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.