Predictive Control Design Based on Neural Model of a Non-linear System
Anna Jadlovská, Nikola Kabakov, Ján Sarnovský · Acta Polytechnica Hungarica · 2008
This paper provides two approaches for design of Generalized Predictive Control (GPC) algorithm for a non-linear dynamic system. In the classical approach of the GPC strategy the recursive method of least square is considered for calculating of the linearized model parameters from the known analytic description of a particular non-linear system. The other purpose of this paper is to show an intelligent approach in which a feed-forward neural network (Multi Layer Perceptron - MLP) is used for modeling the same non-linear system within the frames of the predictive control. The possibility of on-line estimation of an actual parameters from the off-line trained neural model of the non-linear system by means method of the instantaneous linearization in each sample point is considered in GPC algorithm design. The validity of classical and neural GPC strategy is tested by computer simulations in Matlab/Simulink language using architecture of S-functions of the library PredicLib.