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INTELLIGENT CONTROL FOR A DRONE BY SELF-TUNABLE FUZZY INFERENCE SYSTEM

机译:通过自我可调模糊推理系统对无人机的智能控制

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The work describes an automatically on-line Self-Tunable Fuzzy Inference System (STFIS) of a new configuration of mini-flying called XSF (X4 Stationnary Flyer) drone. A Fuzzy controller based on on-line optimization of a zero order Takagi-Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of a quadratic error term and a weight decay term that prevents an excessive growth of parameters. Thus, we carried out control for the continuation of simple trajectories such as the follow-up of straight lines, and complex (half circle, comer) by using the STFIS technique. This permits to prove the effectiveness of the proposed control law. We studied the robustness of the two controllers used in the presence of disturbances. We presented two types of disturbances, the case of a gust of wind and taking into account white noise disturbances. A comparison between the Self-Tunable Fuzzy Inference System (STFIS) and Adaptive Network based Fuzzy Inference System (ANFIS) is given.
机译:该工作描述了一种自动在线自我调谐模糊推理系统(STFIS)的新配置,称为XSF(X4 StateNary Flyer)无人机。通过反向传播算法成功应用了基于零阶Takagi-sugeno模糊推理系统(FIS)的基于在线优化的模糊控制器。它用于最小化由二次误差项组成的成本函数和重量衰减术语,以防止参数的过度增长。因此,我们通过使用STFIS技术对诸如直线的后续行动的简单轨迹的延续进行了控制,以及通过STFIS技术进行复杂的(半圈,复合)。这允许证明拟议的控制法的有效性。我们研究了两个控制器在存在下使用的控制器的稳健性。我们介绍了两种类型的骚乱,阵风的风和考虑到白噪声干扰。给出了自我可调模糊推理系统(STFIS)和基于自适应网络的模糊推理系统(ANFIS)之间的比较。

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