Numerical optimization software for solving stochastic optimal control

Abstract

Stochastic optimal control is a branch of control theory that deals with uncertain system parameters. One of the requirements for an accurate description of systems is a complete knowledge of the structure or values of the basic parameters of the system, which if achieved, the optimization process will also have accurate results. In this paper, we highlight a method for optimizing control under the control of a stochastic uncertainty approach that involves the use and dependence on uncertain parameters for systems whether they are distributed by a distribution function or within other sets of potential values by using the python language. Finally, the numerical results were implemented in python using the GEKKO package.