Infinite-horizon model predictive control with structured input signals

Model predictive control (MPC) is a very popular controller design method in the process industry. One of the main advantages of MPC is that it can handle constraints on the inputs and outputs and it is capable of tracking pre-scheduled reference signals. In this paper the infinite prediction horizon problem is discussed. The input signal has been structured, in order to be able to handle signal constraints, to track pre-scheduled reference signals and to reject measurable disturbances. Beyond a switching horizon, the input signal is described by a number of (orthogonal) basis functions or a static state feedback. By structuring the input, the degrees of freedom in the resulting optimization problem remains bounded. The optimal infinite-horizon model predictive control-law is given in a closed form. In the unconstrained case an expression for the LTI controller is derived.