Recent Advancement of Model Predictive Control in Theory, Algorithms, and Applications

Dear colleagues,

Model Predictive Control (MPC) has been increasingly studied in theory, algorithms, and applications for control techniques in advanced process control, autonomous driving vehicles, smart and virtual grids, power electronics, real-time solutions for very large systems and big data techniques, creative artificial intelligence, IoT based architectures, nano technologies, process industries, newly climate buildings and energies, human central nervous system, human movement, robotics and humanoid robots, electronics and microprocessors, electric motors, cruise control, collaborative robots, remote and tele-operations, self-driving vehicles, etc.

MPC are applied successfully in more than 90% of technical control problems since they can provide the optimal control action for the real-time, predict future system behaviors, and satisfy all dynamic input/output/state constraints while the other control techniques do not have this ability. Furthermore, MPC is the most popular and effective universally implemented as a digital computer science controller online thus far.

As one of model-based control techniques, MPC is now increasing its applications for model-free control techniques such as in fuzzy systems, artificial intelligence, human central nervous systems, fault detection model-based, interacting multiple-model, stochastic models, nano technologies, and human bio-technologies. This Special Issue, therefore, hopes to receive your updated original and review studies on recent advancements of MPC in theory, algorithms, and applications.

On the other hand, MPC is one of the model-based control techniques that is NOT designed for free-model based, imprecise or fault output measurements, uncertain systems, and model-plant mismatches. Recent developments in MPC have overtaken these disadvantages of using updated MPC theory and algorithms. Recent achievements are found in non-linear MPC, adaptive MPC, robust MPC, sliding mode MPC, fuzzy MPC, intelligent uncertainty MPC, genetic MPC, economic MPC, stochastic and neural network MPC, etc.

MPC technologies are a very broad and wide-ranging subject, and we hope to receive your contributions including, but not limited to, new MPC studies on:

• State-of-the-art MPC reviewing and performances;
• Update academic MPC theories;
• Novel MPC algorithms;
• Recent MPC applications
• Nonlinear MPC;
• Adaptive MPC;
• Robust MPC;
• MPC applications in robotics, autonomous, automation, automotive, industries, economies, bio-technologies, nano-technologies, and social and human life aspects.

Thank you very much for your contributions.

Dr. Jaroslav Hlava
Prof. Dr. Trieu Minh Vu
Guest Editors

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