Real-Time Output Feedback Neurolinearization

Document Type : Research Note

Authors

Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, I.R. IRAN

Abstract

 An adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. Another key feature of this structure is the fact that, it does not need model of the system. In this scheme, neurolinearizer has few weights, so it is practical in adaptive situations.  Online training of neurolinearizer is compared to model predictive recurrent training. Relationships between this controller and neural network based model reference adaptive controller are established. A CSTR reactor and pH control in a neutralization process illustrate performance of this method. Simulation studies show a superior performance with respect to a PI controller.

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[1] Peel, C., Willis, M. J., Tham, M. T. and Manchanda,  S., Globally Linearizing Control Using Artificial Neural Networks, Proc. of IEEE Inter. Conf. on Cont., 967 (1994).
[2] Braake, A.A. B., Can, E. J. L.V., Scherpen, J. M. A., Verbruggen, H.B., Control of Nonlinear Chemical Processes Using Neural Models and Feedback Linearization, Computers Chem. Eng., 22, 1113 (1998).
[3] Boozarjomehry, R.B., Svercek, W.Y., Output Feedback Neurolinearizations ISA Transactions, 40,  139 (2001).
[4] Polycarpou, M.M., Stable Adaptive Neural Control Schemes for Nonlinear Systems and Neural Networks, IEEE Trans. Automat. Control, 447 (1996).
[5] Ge, S.S., Hang, C. C., Zhang, T, Nonlinear Adaptive Control Using Neural Networks and its Application to CSTR Systems, Journal of Process Control, 29, 313 (1998).
[6] Ge, S.S., Hang, C. C., Zhang, T., Adaptive Neural Network Control of Nonlinear Systems by State and Output Feedback, IEEE Transactions on Systems, Man, and Cybernetics part B, 818 (1999).
[7] Hovakimyan, N., Yang, B. J., Calise, A. J., Adaptive Output Feedback Control  Methodology Applicable to Non-Minimum  Phase Nonlinear  Systems, Automatica, 42, 513 (2006).
[8]  Krishnapura, V.G. Jutan, A., A Neural Adaptive Controller, Chemical Engineering Science, 55,  3803(2000).
[9] Soroush, M., Kravaris, C., Discrete-Time Nonlinear Controller Synthesis by Input/Output Linearization, AICHE, 38, 1923(1992).
[10] Jagannathan, S., Discrete Time CMAC NN Control of Feedback Linearizable Nonlinear Systems Under Persistence of Excitation, IEEE Transactions on Neural Networks, 10, 128 (1999).
[11] Venkateswalu, Ch., Rao, K.V., Dynamic Recurrent Radial Basis Function Network Model Predictive Control of Unstable Nonlinear Processes, Chemical Engineering Science, 60,  6718 (2005).
[12] Hrycej, T., “Neuro Control Toward an Industrial Control Methodology,” John Wiley & sons, Inc. (1997).
[13] Ahmed, M. S., Neural Net Based MRAC for a Class of Nonlinear Plants, Neural Networks, 112 (2000).
[14] Cybenko, G., Approximation by Superposition of a Sigmoid Function, Mathematics of Control, Signals and Systems, 2(4), 303(1989).
[15] Henson, M.A., Feedback Linearization Strategies for Nonlinear Process Control,PhD. Dissertation, University of California, Santa Barbara (1992).
[16] Psaltis, D., Sideris, A., Andmamura, A Neural Cntroller, Proc. IEEE 1st Int. Conf. on NeuralNetworks, 217 (1987).
[17] McLain R.B., Henson M.A., Principle Component Analysis for Nonlinear Model Reference Adaptive Control, Computers and Chemical Engineering,  99 (2000).
[18] Cheng, J., Yi, J., Zhao, D., Neural Network Based Model Reference Adaptive Control for Ship Steering System, International Journal of Information Technology, 11(6), 75(2005).
[19] Douratsos, I., Gomm, J.B., Neural Network Based Model Reference Adaptive Control for Processed with Time Delay, International Journal of Information and Systems Sciences, 3(1), 161(2007).
[20] Yeo, Y., K., Kwon, T. I., A Neural PID Controller for the pH Neutralization Process, Process Design and Control, 978(1999).
[21] Chen, J., Huang, T. C., Applying Neural Networks to On-Line Updated PID Controllers for Nonlinear Process Control, Journal of Process Control, 211(2004).
[22] Boozarjomehry, R.B., Svercek, W.Y., Aoutomatic Design of Neural Network Structures, Computers and Chemical Engineering, 25, 1075(2001).
[23] Boling, J.M.,  Seborg, D.E.,  Hespanha,  J. P., Multi-Model Adaptive Control of a Simulated pH NeutralizationProcess,ControlEngineering Practice, 15, 663 (2007).
[24] Nahas, E. P., Henson, M.A., Seborg, D.E., Nonlinear Internal Model Control Strategy for Neural Network Models, Comput. Chem. Eng., 16, 1039(1992).