Optimal control problem is one of the most challenging subjects in control theory. It has numerous applications in science and engineering. In this study, we are motivated to obtain the solution of fuzzy optimal control problems via universal approximation capability of a single-layer feedforward artificial neural network. First, we transform the fuzzy optimal control problems into systems of first-order ordinary differential equations via fuzzy Pontryagin’s minimum principle and fuzzy Hamiltonian function. Then, we solve these systems by using a single-layer feedforward sigmoid-weighted neural network. The numerical examples are presented to determine the simplicity and efficiency of the proposed method.

最优控制问题是控制理论中最具挑战性的课题之一。它在科学和工程中有许多应用。在这项研究中，我们有动力通过单层前馈人工神经网络的通用逼近能力来获得模糊最优控制问题的解决方案。首先，通过模糊庞特里亚金的最小原理和模糊哈密顿函数将模糊最优控制问题转化为一阶常微分方程组。然后，我们通过使用单层前馈S型加权神经网络来解决这些系统。数值例子表明了该方法的简单性和有效性。