Model Predictive Control: Mathematical Foundations and Emerging Engineering Challenges
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".
Deadline for manuscript submissions: 20 May 2026
Special Issue Editors
Interests: model predictive control; reinforcement learning; learning-based control; intelligent transportation system; traffic management
Special Issue Information
Dear Colleagues,
Model predictive control (MPC) has become a cornerstone in modern control engineering, offering a versatile framework that balances system constraints, optimization objectives, and prediction-based feedback. As the complexity and scale of engineering systems continue to grow—ranging from autonomous vehicles and energy systems to robotics and process industries—MPC faces new challenges in mathematical formulation, computational feasibility, and real-world deployment.
This Special Issue aims to collect original research and review articles addressing the mathematical foundations, computational methods, and engineering implementations of MPC. We particularly welcome works that explore robust/stochastic MPC, data-driven and learning-based approaches, distributed and embedded MPC architectures, and applications that reveal new insights into system-level challenges.
Topics of interest include, but are not limited to, the following:
- Robust and stochastic model predictive control
- Data-driven and learning-based MPC (e.g., RL-MPC, Koopman-based MPC)
- Distributed, hierarchical, or large-scale MPC frameworks
- Fast optimization algorithms and real-time implementations
- Embedded MPC in robotics, vehicles, and industrial systems
- Applications of MPC in transportation, energy, aerospace, and process control
- Integration of MPC with digital twins or cyber–physical systems
- Safety-critical MPC and constraint handling under uncertainty
We look forward to your valuable contributions that advance both theoretical insights and practical applications of MPC.
Dr. Dingshan Sun
Dr. Jia Xu
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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
- model predictive control
- robust and stochastic control
- learning-based control
- real-time optimization
- cyber–physical systems
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