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.