During the last couple of years, the importance of the control systems has been increased significantly. The modern technologies can be very complex systems with many measurement and actuation points. It is required to operate these complex systems within the specified technological and economical limits. Therefore, there is a rapidly increasing need for precise, safe, reliable and user-friendly control systems. It has been confirmed by a huge number of successful applications in different areas, that Model based Predictive Control (MPC) approach is able to meet a great number of such requirements and therefore it is becoming a very popular method in a large number of control applications. The aim of this intensive course is to present some of the key areas in the MPC research, development and applications. 

• Introduction
  • Motivation for advanced control techniques
  • Brief history of model predictive control
• Linear Model Predictive Control
  • Formulation of linear model predictive control (model, criterion, constraints, receding horizon, optimization problem, etc.)
  • Analysis of linear model predictive control (infinite prediction horizon, terminal penalty, terminal set, positively invariant sets, stability, robustness)
  • Hybrid systems (definition, mixed continuous/discrete modeling)
  • Optimization algorithms for linear MPC (performance analysis, active set algorithms, interior point algorithms, tailoring for MPC, etc.)
• Nonlinear Model Predictive Control
  • Formulation of nonlinear MPC (DAE models, criterion, constraints, nonlinear optimization problem)
  • Analysis of nonlinear MPC (stability, robustness)
  • Numerical methods for nonlinear MPC (optimization problem, nonlinear least squares, direct multiple shooting methods, sequential quadratic programming)
• Model Predictive Control from practical point of view
  • Practical formulations of MPC
  • Development cycle of industrial MPC
  • Demonstration of MPC controller application

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