针对现有混沌系统在实际应用中的性能不足问题，提出一种结合区间二型模糊大脑情感学习（interval type-2 fuzzy brain emotional learning, IT2FBEL）控制器与鲁棒控制器实现超混沌系统同步控制的方法。该方法通过IT2FBEL控制器逼近超混沌系统中的未知项，利用梯度下降法对IT2FBEL控制器的权重及参数进行在线更新，实现超混沌主系统对于从系统的同步追踪。同时，鲁棒控制器用于处理系统的残余误差，使得控制器的输出值尽可能逼近理想控制值，进一步提高超混沌系统的同步精度。仿真结果表明，该方案能够实现超混沌系统的高度同步，与RBF神经网络，BP神经网络和BEL模型相比，拥有较好的跟踪性能和计算效率。此外，进行了语音安全传输与图像安全传输的仿真实验，结果说明了该方法应用于保密通信邻域的有效性与适应性，为混沌保密通信的实际应用提供进一步的理论支持。

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.

TP183???????

厦门市自然科学基金（编号：3502Z20227215），厦门市海洋与渔业发展专项资金青年科技创新项目（23ZHZB043QCB37），厦门理工学院高层次人才科研启动项目(YKJ22060R)。