Aiming at the problem that the performance of existing chaotic systems is insufficient in practical applications, this paper proposes a method to realize the synchronization control of hyperchaotic systems by combining interval type-2 fuzzy brain emotional learning (IT2FBEL) controller and robust controller. In this method, the IT2FBEL controller is used to approximate the unknown items in the hyperchaotic system, and the gradient descent method is used to update the weight and parameters of the IT2FBEL controller online to achieve the synchronous tracking of the hyperchaotic master system to the slave system. The robust controller is used to deal with the residual error of the system, making the output value of the controller approach the ideal control value as much as possible, and further improving the synchronization accuracy of the hyperchaotic system. Simulation results demonstrate that this scheme can achieve high synchronization of hyperchaos system and has better tracking performance and computational efficiency than RBF neural network, BP neural network and Brain Emotional Learning models. Additionally, simulation experiments were conducted for secure transmission of voice and image data. The results demonstrate the effectiveness and adaptability of the proposed method in the domain of confidential communication, providing further theoretical support for the practical application of chaotic secure communication.