This paper presents a numerical fuzzy indirect method based on the fuzzy basis functions technique to solve an optimal control problem governed by Poisson’s differential equation. The considered problem may or may not be accompanied by a control box constraint. The first-order necessary optimality conditions have been derived, which may contain a variational inequality in function space. In the presented method, the obtained optimality conditions have been discretized using fuzzy basis functions and a system of equations introduced as the discretized optimality conditions. The derived system mostly contains some nonsmooth equations and conventional system solvers fail to solve them. A fuzzy system-based semi-smooth Newton method has also been introduced to deal with the obtained system. Solving optimality systems by the presented method gets us unknown fuzzy quantities on the state and control fuzzy expansions. Finally, some test problems have been studied to demonstrate the efficiency and accuracy of the presented fuzzy numerical technique.
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Azizi, M., Amirfakhrian, M., Fariborzi Araghi, M. (2022). Application of fuzzy systems on the numerical solution of the elliptic PDE-constrained optimal control problems. Computational Methods for Differential Equations, 10(2), 351-371. doi: 10.22034/cmde.2021.39351.1725
MLA
Masoomeh Azizi; Majid Amirfakhrian; Mohammad Ali Fariborzi Araghi. "Application of fuzzy systems on the numerical solution of the elliptic PDE-constrained optimal control problems". Computational Methods for Differential Equations, 10, 2, 2022, 351-371. doi: 10.22034/cmde.2021.39351.1725
HARVARD
Azizi, M., Amirfakhrian, M., Fariborzi Araghi, M. (2022). 'Application of fuzzy systems on the numerical solution of the elliptic PDE-constrained optimal control problems', Computational Methods for Differential Equations, 10(2), pp. 351-371. doi: 10.22034/cmde.2021.39351.1725
VANCOUVER
Azizi, M., Amirfakhrian, M., Fariborzi Araghi, M. Application of fuzzy systems on the numerical solution of the elliptic PDE-constrained optimal control problems. Computational Methods for Differential Equations, 2022; 10(2): 351-371. doi: 10.22034/cmde.2021.39351.1725