Explanation of the predictive controller and the effect of its tuning on the control quality

Abstract

The Model Predictive Control (MPC) concept and its realization is explained together with some real-world laboratory application examples. Usually, future control errors and control action changes are penalized in the cost function (objective) by predictive controllers. The cost function can be seen as a function of future control actions utilizing the process model in a form of the predictor. A derivation of the predictors for the transfer function (external) and state-space (internal) model is indicated in the paper. An analytical solution to the given optimization problem is possible in an unconstrained case. A quadratic programming strategy must be used in case of the occurrence of the process constraints that should be respected by the controller. The authors apply both algorithms to two types of dynamical systems – to proportional (stable) system and to integrating (unstable) system and they demonstrate the influence of the penalization parameters (weights) in the cost function on the control quality. The authors aim to high-light the MPC strategy and its potential, and on the other hand, mention some bottlenecks and risks associated with model-optimisation-based methods.