Abstarct: Abstract In this paper, we present an application of optimal control and neural networks to chaos control and synchronization. In the second chapter, a new parallel synchronization method for chaotic systems is presented. By transforming the synchronization problem into an optimal control problem, we can define a suitable error function, such that by minimizing it using a fuzzy neural network using radial functions, the synchronization error approaches zero, while the enabling control is achieved. In the third chapter, an intelligent scheme called the generalized hyperbolic fuzzy model is used for the synchronization problem of chaotic differential equations with a master system and several slave systems. In the first step, an infinite horizon optimal control problem related to the synchronization problem is constructed using an enabling control strategy. Then, using a suitable transformation, the infinite horizon optimal control problem is transformed horizon problem. According to the Pontryagin maximum principle, the necessary optimality conditions for the finite horizon problem are investigated in the form of a two-point boundary value problem. Here, for the first time, generalized hyperbolic fuzzy models are used to approximate the solutions of the two-point boundary value problem. This generalized hyperbolic fuzzy model uses the number of sample points as the training data set, and the Levenberg-Marquardt algorithm is selected as the optimizer. Relying on the ability of the generalized hyperbolic fuzzy model as a function approximator, the experimental solutions for the variables (state, adjoint, and control) in the two-point boundary value problem are substituted. Then, the obtained system of nonlinear algebraic equations is transformed into an error function minimization problem. A learning scheme baxsed on the Levenberg-Marquardt algorithm is employed as the optimizer to extract the adjustable parameters of the fuzzy solutions. Finally, synchronization and control for Bayer-Sahle hyperchaotic flow of arbitrary dimensions with unknown parameters will be investigated. Using the the active control method, parameter update rules, and visual hybrid synchronization between two identical hyperchaotic systems with completely unknown parameters are developed. This approach allows controlling Bayer-Sahle hyperchaotic flow of arbitrary dimensions up to unstable equilibrium points.