In this article, a new intelligent hybrid controller is proposed. The controller is based on the combination of the orthogonal endocrine neural network (OENN) and orthogonal endocrine ANFIS (OEANFIS). The orthogonal part of the controller consists of Chebyshev orthogonal functions, which are used because of their recursive property, computational simplicity, and accuracy in nonlinear approximations. Artificial endocrine influence on the controller is achieved by introducing excitatory and inhibitory glands to the OENN part of the structure, in the form of postsynaptic potentials. These potentials provide a network with the capability of additional self-regulation in the presence of external disturbances. The intelligent structure is trained using a developed learning algorithm, which consists of both offline and online learning procedures: online learning for fitting OENN substructure and offline learning for adjusting OEANFIS parameters. The learning process is expanded by introducing the learning rate adaptation algorithm, which bases its calculations on the sign of the error difference. Finally, the proposed intelligent controller was experimentally tested for control of a nonlinear multiple-input_multiple-output two rotor aerodynamical system. During the test phase, an additional four related intelligent control logics and default PID-based controllers were used, and tracking performance comparisons were performed. The proposed controller showed notably better online results in comparison to other control algorithms. The major deficiencies of the structure are complexity and noticeably large training computation time, but these drawbacks can be neglected if tracking performances of a dynamical system are of the highest importance.

中文翻译：

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正交内分泌智能控制器非线性MIMO系统的自适应控制


本文提出了一种新型智能混合控制器。该控制器基于正交内分泌神经网络（OENN）和正交内分泌ANFIS（OEANFIS）的组合。控制器的正交部分由切比雪夫正交函数组成，由于其递归特性、计算简单性和非线性近似的准确性而被使用。对控制器的人工内分泌影响是通过以突触后电位的形式将兴奋性和抑制性腺体引入结构的 OENN 部分来实现的。这些电位为网络提供了在存在外部干扰时额外自我调节的能力。智能结构使用开发的学习算法进行训练，该算法由离线和在线学习程序组成：用于拟合OENN子结构的在线学习和用于调整OEANFIS参数的离线学习。通过引入学习率自适应算法来扩展学习过程，该算法的计算基于误差差的符号。最后，对所提出的智能控制器进行了非线性多输入多输出两转子气动系统控制的实验测试。在测试阶段，使用了另外四个相关的智能控制逻辑和默认的基于PID的控制器，并进行了跟踪性能比较。与其他控制算法相比，所提出的控制器显示出明显更好的在线结果。该结构的主要缺陷是复杂性和明显大量的训练计算时间，但如果动态系统的跟踪性能是最重要的，那么这些缺陷可以忽略不计。