Model Predictive Control (MPC) is a well-established technique for controlling multivariable systems subject to constraints on manipulated variables and outputs in an optimized way. Following a long history of success in the process industries, in recent years MPC is rapidly expanding in several other domains, such as in the automotive and aerospace industries, smart energy grids, and financial engineering. The course teaches the theory and practice of Model Predictive Control (MPC) of constrained linear, linear time-varying, nonlinear, stochastic, and hybrid dynamical systems, and numerical optimization methods for the implementation of MPC, including the use of the MPC Toolbox for MATLAB for basic linear MPC, and of the Hybrid Toolbox for explicit and hybrid MPC.
Topics covered in the course: General concepts of Model Predictive Control (MPC); MPC based on quadratic programming; General stability properties; MPC based on linear programming; Models of hybrid systems: discrete hybrid automata, mixed logical dynamical systems, piecewise affine systems; MPC for hybrid systems based on on-line mixed-integer optimization; Multiparametric programming and explicit linear MPC, explicit solutions of hybrid MPC; Stochastic MPC based on scenario enumeration; Linear parameter- and time-varying MPC and applications to nonlinear dynamical systems; Selected applications of MPC in various domains, with practical demonstration of the MATLAB toolboxes.
Prerequisites: Linear algebra and matrix computation, linear control systems, numerical optimization.