Journal
APPLIED SOFT COMPUTING
Volume 24, Issue -, Pages 1136-1144Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2014.08.006
Keywords
Differential algebraic equation; Genetic programming; Matrix Riccati differential equation; Runge-Kutta method; Optimal control and Stochastic linear quadratic singular neuro Takagi-Sugeno fuzzy system
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Funding
- HIR grant [UM.C/625/1/HIR/MOHE /SC/13]
- UMRG grant [RG099/10AFR]
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In this paper, optimal control for stochastic linear quadratic singular neuro Takagi-Sugeno (T-S) fuzzy system with singular cost is obtained using genetic programming(GP). To obtain the optimal control, the solution of matrix Riccati differential equation (MRDE) is computed by solving differential algebraic equation (DAE) using a novel and nontraditional GP approach. The obtained solution in this method is equivalent or very close to the exact solution of the problem. Accuracy of the solution computed by GP approach to the problem is qualitatively better. The solution of this novel method is compared with the traditional Runge-Kutta (RK) method. A numerical example is presented to illustrate the proposed method. (C) 2014 Elsevier B.V. All rights reserved.
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