Approximate state-space model predictive control

This paper is concerned with a Model Predictive Control (MPC) algorithm for dynamic systems described by nonlinear state-space models. A unique feature of the algorithm is the fact that the current value of the manipulated variable (i.e. the decision variable of MPC) is not calculated from an optimisation problem, but from an analytical linear control law. The coefficients of the control law, due to a nonlinear nature of the process, are time-varying. They are found on-line by an approximator (a neural network is used for this purpose). The approximator is trained off-line in such a way that the resulting MPC algorithm mimics the suboptimal MPC technique with online model linearisation. Thanks to such an approach, successive on-line model linearisation is not successively performed and some other calculations are not necessary. For a polymerisation reactor off-line training of the approximator is described and the approximate algorithm is compared with the classical MPC algorithms with on-line model linearisation.