Optimal control for stochastic linear quadratic singular neuro Takagi–Sugeno fuzzy system with singular cost using genetic programming

• Published in: Applied soft computing Vol. 24; pp. 1136 - 1144
• Main Authors: Kumaresan, N., Ratnavelu, Kuru
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2014
• Subjects: 03 E 72; 49 K 45; 68 N 19; 92 D 10; 93 B 52; 93 E 20; 94 D 05; Differential algebraic equation; Genetic programming; Matrix Riccati differential equation; Optimal control and Stochastic linear quadratic singular neuro Takagi–Sugeno fuzzy system; Runge–Kutta method; 93 E 20; Runge–Kutta method; Optimal control and Stochastic linear quadratic singular neuro Takagi–Sugeno fuzzy system; 92 D 10; Differential algebraic equation; Matrix Riccati differential equation; 03 E 72; Genetic programming; 94 D 05; 93 B 52; 49 K 45; 68 N 19
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2014.08.006

•Takagi-Sugeno fuzzy model.•Neuro-fuzzy system.•Nontraditional genetic programming approach.•Optimal control for Stochastic linear singular Takagi–Sugeno fuzzy singular system. 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.