TY - Jour A2 - Zargarzadeh，Hassan Au - Ibraheem，Ghusn Abdul Redha Au - Azar，Ahmad Taher Au - Ibraheem Kasim Au - Humaadi，Amjad J. Py - 2020 da - 2020/04/29 - 这是一种新颖的设计用于使用杂交果蝇的移动机器人的基于神经网络的分数PID控制器和粒子群优化SP - 3067024 VL-2020 AB - 用于工程应用的群体优化的分数控制的设计是优化分析中的主动研究主题。这项工作提供了基于新的神经网络（NN）非线性分数控制结构的分析，设计和仿真。在隐藏和输出层中使用非线性和线性激活功能的合适布置，分别在不同隐藏层神经元之间的适当连接权重，新类非线性神经分数比例整体衍生物（NNFOPID）控制器提出和设计。通过近似FoPID控制器的分数衍生和积分作用并应用于非完整差分驱动移动机器人（DDMR）的运动控制来获得。除了分数积分和分数衍生阶数之外，所提出的NNFoPID控制器的参数包括衍生，积分和比例的增益。这些参数的调谐使得这种控制器的设计比古典PID更困难。为了解决这个问题，通过改进的自适应粒子群优化（Mapso）的杂交和增强的水果飞行优化（Effo）来调整NNFoPID控制器的参数，提出了一种新的群优化算法，即Mapso-Effo算法，即提出了一种新的Sparm优化算法。首先，我们通过添加具有大量粒子的初始运行阶段，开发了修改的自适应粒子群优化（MAPSO）算法。 Secondly, the conventional fruit fly optimization (FFO) algorithm has been modified by increasing the randomness in the initialization values of the algorithm to cover wider searching space and then implementing a variable searching radius during the update phase by starting with a large radius which decreases gradually during the searching phase. The tuning of the parameters of the proposed NNFOPID controller is carried out by reducing the MS error of 0.000059, whereas the MSE of the nonlinear neural system (NNPID) is equivalent to 0.00079. The NNFOPID controller also decreased control signals that drive DDMR motors by approximately 45 percent compared to NNPID and thus reduced energy consumption in circular trajectories. The numerical simulations revealed the excellent performance of the designed NNFOPID controller by comparing its performance with that of nonlinear neural (NNPID) controllers on the trajectory tracking of the DDMR with different trajectories as study cases. SN - 1076-2787 UR - https://doi.org/10.1155/2020/3067024 DO - 10.1155/2020/3067024 JF - Complexity PB - Hindawi KW - ER -