Some Applications for Nonlinear Processes of a Model Based Predictive Control Algorithm

Model Based Predictive Control (MBPC) is a class of computer algorithms that explicitly use a process model to predict future plant outputs and compute an appropriate control action through on-line optimization of a cost objective function over a future horizon, subject to various constraints. This paper presents an MBPC type algorithm applied to nonlinear processes. The basic idea of the algorithm is the on-line simulation of the future behavior of the control system, by using a few candidate control sequences. Then, using rule based control these simulations are used to obtain the `optimal' control signal. The efficiency and applicability of the proposed algorithm for nonlinear processes are demonstrated through applications.