Approximate Model Predictive Control for Nonlinear Multivariable Systems

The control of multi-input multi-output (MIMO) systems is a common problem in practical control scenarios. However in the last two decades, of the advanced control schemes, only linear model predictive control (MPC) was widely used in industrial process control (Maciejowski, 2002). The fundamental common idea behind all MPC techniques is to rely on predictions of a plant model to compute the optimal future control sequence by minimization of an objective function. In the predictive control domain, Generalized Predictive Control (GPC) and its derivatives have received special attention. Particularly the ability of GPC to be applied to unstable or time-delayed MIMO systems in a straight forward manner and the low computational demands for static models make it interesting for many different kinds of tasks. However, this method is limited to linear models.