Title: Fuzzy-neural predictive control using fast optimisation polices

Authors: Margarita Terziyska; Yancho Todorov

Addresses: Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev St., Block 2, 1113, Sofia, Bulgaria ' Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev St., Block 2, 1113, Sofia, Bulgaria

Abstract: This paper describes the development of fast optimisation polices based on Newtonian approaches, as effective algorithms to solve the on-line optimisation task, during the operation of a predictive controller. To simplify the calculation of the control actions, an iterative solutions based on Newton-Raphson and Levenberg-Marquardt approaches, are proposed. To avoid the computational load related to Hessian inversion, a simple Gaussian elimination in a form of matrix decomposition is applied. As plant response predictor, a Takagi-Sugeno fuzzy-neural network, with global and local (after the rules layer) recurrent nodes, is used. The efficiency of the proposed optimisation strategies is demonstrated by simulation experiments in MATLAB environment to control a continuous stirred tank reactor.

Keywords: Takagi-Sugeno fuzzy-neural networks; nonlinear control; predictive control; optimisation; gradient descent; Newton method; Levenberg-Marquardt method; fuzzy logic; neural networks; matrix decomposition; simulation; continuous stirred tank reactors.

DOI: 10.1504/IJRIS.2014.066250

International Journal of Reasoning-based Intelligent Systems, 2014 Vol.6 No.3/4, pp.136 - 144

Available online: 09 Dec 2014 *

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