Neural network and fuzzy logic-based hybrid attitude controller designs of a fixed-wing UAV
Neural network and fuzzy logic-based hybrid attitude controller designs of a fixed-wing UAV
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DOI：
10.1007/s00521-020-05629-5
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发表时间：
2021-01
影响因子：
6
通讯作者：
Şaban Ulus;Ikbal Eski
中科院分区：
文献类型：
--
作者：
Şaban Ulus;Ikbal Eski
本文提出了一种小型无人机的设计方案，并将其应用于农业领域的农药喷洒和杂草控制。根据文献综述，比例+积分+微分（PID）结构被用于控制许多这些无人机。这种控制器是不足以对付不确定的天气条件和干扰的影响。在这项研究中，许多不同的控制技术进行评估，以选择控制器的结构，可以响应这些不确定性。具有最佳效果的结构被选为无人机控制器。采用Ultrastick-25 e微型无人机模型进行横滚和偏航角横向动力学控制。阐述了无人机纵向和横向动力学的状态空间表示，并给出了在60 km/h飞行速度条件下无人机姿态控制的横向动力学。根据副翼和方向舵的输入，分别采用经典PID、人工神经模糊推理系统（ANFIS）、模糊逻辑控制器、ANFIS-PID组合控制器和PD-Fuzzy-PI控制器等五种控制方法进行了横向动力学仿真。此外，假设三个不同的输入信号来评估系统响应。此外，对所设计的不同控制器的瞬态响应和时间性能参数，如超调，峰值，上升和建立时间，稳态误差进行了分析。仿真结果表明，PD-fuzzy-PI和ANFIS-PID控制器在稳定水平飞行条件下具有较好的控制性能。本研究所获得的模拟结果将有助于实验研究。
In this paper, a mini unmanned aerial vehicle (UAV) is planned to be used in applications such as spraying pesticide and weed control in agricultural areas. According to literature review, proportional + integral + derivative (PID) structure is used to control many of these UAVs. This controller is insufficient against uncertain weather conditions and disturbance effects. In this study, many different control techniques are evaluated to select the controller structure that can respond to these uncertainties. The structure having the best result was chosen as the UAV controller. Ultrastick-25e mini UAV model is used to control the roll and yaw angle lateral dynamics. State-space presentation of the UAV longitudinal and lateral dynamics is explained, and it is just obtained for the lateral dynamics to control the attitude of the UAV under 60 km/h flight velocity condition. According to the aileron and rudder inputs, lateral dynamics simulations have successfully done by using five different controller methods such as classical PID, artificial neuro-fuzzy inference system (ANFIS), fuzzy logic controller, combined ANFIS-PID, and PD-Fuzzy-PI controllers. Moreover, three different input signals are assumed to evaluate the system response. Additionally, transient response and the time performance parameters such as overshoots, peak, rise and settling times, and steady-state error have analyzed for the designed different controllers. The simulated results for the five different controller designs showed that combined PD-fuzzy-PI and ANFIS-PID controllers have more acceptable performance than other controllers at the steady level flight condition. It is aimed that the simulation findings obtained in this study will contribute to experimental studies.