Model Predictive Control and Optimization Applied to Process Control

Dear Colleagues,

Predictive control (MPC) is one of the advanced control techniques with a significant impact on industry. This technique has been used intensively since the emergence of DMC (Dynamic Matrix Control) in the 1980s in the petrochemical industries until its current expansion in sectors as diverse as aerospace, energy saving, irrigation, power generation, logistics or urban transportation. The main reasons for the success of this technique in industrial process control are:

• Handling multivariable processes in an implicit way;
• Possibility of taking into account the constraints of system variables;
• Incorporation of prediction models and trajectories;
• Versatility in adjusting controller parameters.

However, the most remarkable feature is the ability of the MPC to calculate the optimal control actions, taking into account all process constraints, since these are directly associated with physical limitations of the actuators, economic costs, energy, etc. The MPC is able to do this by employing online optimization algorithms that calculate these actions at every iteration. Obviously, the use of optimization routines involves a high computing cost, which usually restricts its application to environments with high-performance computers or slow dynamic processes. That question evidences that there is a significant gap between the state-of-the-art research and applications in the MPC field.

The objective of this Special Issue is to seek high-quality submissions that highlight emerging approaches and applications to answer some of the open questions in the field of model predictive control and optimization applied to process control. The topics of interest include but are not limited to:

• Nonlinear predictive control of hybrid systems;
• Multimodal nonlinear predictive control;
• Stochastic predictive control;
• Fuzzy and neural network predictive control;
• Adaptative predictive control;
• Economic predictive control;
• Predictive control for fast dynamics;
• Explicit predictive control;
• Optimization algorithms for model predictive control;
• Heuristic optimization for model predictive control;
• Real industrial applications;
• Real-time model predictive implementation;
• Machine learning and artificial intelligence for model predictive control.

Prof. Dr. Sergio Garcia-Nieto
Prof. Dr. Javier Sanchis
Guest Editors

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• Nonlinear predictive control of hybrid systems
• Multimodal nonlinear predictive control
• Stochastic predictive control
• Fuzzy and neural network predictive control
• Adaptative predictive control
• Economic predictive control
• Predictive control for fast dynamics
• Explicit predictive control
• Optimization algorithms for model predictive control
• Heuristic optimization for model predictive control
• Real industrial applications
• Real-time model predictive implementation
• Machine learning and artificial intelligence for model predictive control.