Neural Network Model Predictive Control of Nonlinear Systems Using Genetic Algorithms

Vesna Rankovic, Jasna Radulovic, Nenad Grujovic, Dejan Divac


In this paper the synthesis of the predictive controller for control of the nonlinear object is considered. It is supposed that the object model is not known. The method is based on a digital recurrent network (DRN) model of the system to be controlled, which is used for predicting the future behavior of the output variables. The cost function which minimizes the difference between the future object outputs and the desired values of the outputs is formulated. The function ga of the Matlab’s Genetic Algorithm Optimization Toolbox is used for obtaining the optimum values of the control signals. Controller synthesis is illustrated for plants often referred to in the literature. Results of simulations show effectiveness of the proposed control system.


model predictive control, nonlinear system, identification, digital recurrent network, genetic algorithm

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