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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S. Jagannathan, F.L. Lewis, Identification of Nonlinear Dynamical Systems Using Multilayered Neural Networks, Automatica, 32(12):1707-1712, 1996.

G. Kenne, T. Ahmed-Ali, F. Lamnabhi Lagarrigue, H. Nkwawo, Nonlinear systems parameters estimation using radial basis function network, Control Engineering Practice, 14(7):819-832, 2006.

J.I. Canelon, L. Shieh, N.B. Karayiannis, A new approach for neural control of nonlinear discrete dynamic systems, Information Sciences, 174(3-4):177-196, 2005.

W. Yu, Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms, Information Sciences, 158(1):131-147, 2004.

S.-K. Oh, W. Pedrycz, H.-S. Park, Hybrid identification in fuzzy-neural networks, Fuzzy Sets and Systems, 138(2):399-426, 2003.

A. Yazdizadeh, K. Khorasani, Adaptive time delay neural network structures for nonlinear system identification, Neurocomputing, 47(1-4):207-240, 2002.

M. Onder Efe, O. Kaynak, A comparative study of neural network structures in identification of nonlinear systems, Mechatronics, 9(8):287-300, 1999.

V. Rankovi’c, I. Nikoli’c, Identification of Nonlinear Models with Feedforward Neural Network and Digital Recurrent Network, FME Transactions, 36(2):87-92, 2008.

S. Dubljevic, P. Mhaskar, N.H. El-Farra, P.D. Christofides, Predictive control of transport-reaction processes, Computers and Chemical Engineering, 29(11-12):2335-2345, 2005.

P. Mhaskar, N.H. El-Farra, P.D. Christofides, Robust hybrid predictive control of nonlinear systems, Automatica, 41(2):209-217, 2005.

H. Peng, T. Ozaki, Y. Toyoda, K. Oda, Exponential ARX model-based long-range predictive control strategy for power plants, Control Engineering Practice, 9(12):1353-1360, 2001.

V. Rankovi’c, I. Nikoli’c, Model Predictive Control Based on the Takagi-Sugeno Fuzzy Model, Journal of Information, Control and Management Systems, 5(1):101-110, 2007.

M. Lazar, O. Pastravanu, A neural predictive controller for non-linear systems, Mathematics and Computers in Simulation, 60(3-5):315-324, 2002.

C.-H. Lu, C.-C. Tsai, Generalized predictive control using recurrent fuzzy neural networks for industrial processes, Journal of Process Control, 17(1):83-92, 2007.

K. Laabidi, F. Bouani, M. Ksouri, Multi-criteria optimization in nonlinear predictive control, Mathematics and Computers in Simulation, 76(5-6):363-374, 2008.

M. Hagan, O.D. Jesus, R. Schultz, Training Recurrent Networks for Filtering and Control, Chapter 11 of Recurrent Neural Networks: Design and Applications, L.R. Medsker and L.C. Jain, Eds., CRC Press, 325-354, 1999.

R.K. Al Seyab, Y. Cao, Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation, Journal of Process Control, 18(6):568-581, 2008.

D.R. Hush, B.G. Horne, Progress in supervised neural networks, IEEE Signal Processing Magazine, 10(1):8-39, 1993.

K.L. Funahashi, Y. K.L. Funahashi, Y. Nakamura, Approximation of dynamical systems by continuous time recurrent neural networks, Neural Networks, 6(6):183-192, 1993.

L. Jin, P. Nikiforuk, M. Gupta, Approximation of discrete-time state-space trajectories using dynamic recurrent neural networks, IEEE Transactions on Automatic Control, 40(7):1266-1270, 1995.

P.S. Sastry, G. Santharam, K.P. Unnikrishnan, Memory Neuron Networks for Identification and Control of Dynamical Systems, IEEE Transactions on Neural Net-works, 5(2):306-319, 1994.


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