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Grey Wolf Optimizer in Design Process of Stable Neural Controller - Theoretical Background and Experiment

机译：灰狼优化器在稳定神经控制器设计过程中 - 理论背景和实验

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This article deals with an adaptive neural controller applied for a nonlinear plant with time-varying parameters. The structure of the controller is based on Radial Basis Function Neural Network. The output part of the controller (weights) is modified in several iterations of the control structure. In this application, the coefficients of the Gaussian functions are constant (it means the centers and width). The relevance of proper selection of those values is presented in tests performed for a real plant (an electrical drive). Moreover, for optimization of this part of the controller the metaheuristic - Grey Wolf Optimizer - algorithm was applied. The centers were selected in a clustering process. The synthesis of the controller includes stability analysis (using the Lyapunov method). The content of this article can be divided into two basic parts, the first shows theoretical considerations and the second is related to the experimental tests of the analyzed neural controller (executed in a laboratory, for the drive with 0.5 kW nominal power, using dSPACE card).
机译：本文涉及具有时变参数的非线性工厂的自适应神经控制器。控制器的结构基于径向基函数神经网络。控制器（权重）的输出部分在控制结构的几次迭代中被修改。在本申请中，高斯函数的系数是恒定的（这意味着中心和宽度）。 The relevance of proper selection of those values is presented in tests performed for a real plant (an electrical drive).此外，为了优化控制器的这一部分，应用了成群质 - 灰狼优化器算法。该中心被选中在聚类过程中。控制器的合成包括稳定性分析（使用Lyapunov方法）。本文的内容可以分为两个基本部分，第一部分显示了理论考虑，第二部分与分析的神经控制器（在实验室中执行的实验室执行的实验测试有关，使用DSPACE卡）。

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