共查询到19条相似文献,搜索用时 78 毫秒
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基于四旋翼飞行器的结构和飞行原理,本文建立了其飞行动力学数学模型,并采用反馈线性化原理对该模型进行精确线性化;同时,本文采用基于趋近律的滑模变结构控制方法,进行飞行控制器设计,并用simulink对设计的控制器进行仿真,实现了四旋翼飞行器的定高悬停控制,提高了其飞行性能和鲁棒性。 相似文献
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为了实现对四旋翼飞行器的稳定飞行控制,对四旋翼飞行器建立了动力学数学模型,并采用准LPV法将非线性模型线性化,在建立的动力学模型基础上,对飞行器垂直速率、俯仰速率、横滚速率、偏航速率四个独立通道上分别设计了PID控制器.并通过Matlab/Simulink软件进行控制系统仿真,并对仿真结果进行分析,仿真结果验证了PID算法的有效性. 相似文献
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《Mechatronics》2022
This paper presents the design of a visual control formulated on an adaptive sliding mode controller for a quadrotor executing a target tracking task subject to disturbances. An image projection of the target from a virtual camera approach, and an image-based visual servoing technique are considered to obtain a singularity-free set of image features to control the position and yaw of the rotorcraft. While, an adaptive sliding mode strategy improves the robustness against bounded external perturbations and uncertainties and provides adaptivity to the visual servoing scheme. Furthermore, an analysis based on Lyapunov theory provides sufficient conditions that guarantee the stability of the closed-loop system. A comparison of the proposed adaptive visual servoing against two recent visual servoing strategies is provided, showing superiority in simulation results. Finally, experimental tests of a Parrot AR.Drone 2.0 tracking a static and moving target further demonstrates the advantages and performance. 相似文献
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《现代电子技术》2017,(24):94-99
及时有效地获取空气质量数据是大气环境保护的前提和基础。针对现有固定点监测手段空间覆盖度不足、成本高、灵活性差等问题,设计一种基于小型四旋翼无人机为飞行平台,Arduino UNO开发板外接MG811,DSM501A,DHT11,MQ-7传感器为硬件平台,Arduino IDE编译器为软件平台的监测仪。采用接触式周期采样方法获取环境数据模拟值,建立与传感器灵敏度关系,使用Matlab中CURVE FITTING TOOL对传感器灵敏度与被测物浓度关系进行拟合分析。拟合后,可决系数大于0.98,方差小于0.1。该自主设计的监测仪具有成本低、易操作、灵活方便的优点,并可扩展其他类型传感器以满足不同的测量环境要求。 相似文献
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为了利用视觉图像中信息的无源性、实时性以及机载控制器的自创性等特性,解决无人机信源易干扰、有延时、受制约的问题,分析了“十”字型四旋翼无人机的动力结构、控制原理以及无人机飞行过程中位姿方程、动力方程之间的相互关系,完成了四旋翼无人机六自由度信息和飞行控制四元素输入信息之间的转换,设计了基于合作目标匹配的无人机视觉图像自主控制算法。结果表明,在实测值为零时,即可完成自主降落功能。该算法可以实现简单环境下四旋翼无人机的自主降落。这一研究对无人机的自主化、智能化发展具有一定的帮助作用。 相似文献
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《Mechatronics》2000,10(1-2):239-263
In this paper, a synergistic combination of neural networks with sliding mode control (SMC) methodology is proposed. As a result, the chattering is eliminated and error performance of SMC is improved. In the approach, two parallel Neural Networks (NNs) are utilized to realize a neuro-SMC. The equivalent control and the corrective control terms of SMC are the outputs of the NNs. The weight adaptations of NNs are based on the SMC equations in such a way that the use of the gradient descent method minimizes the control activity and the amount of chattering while optimizing the error performance. The approach is almost model-free, requiring a minimal amount of a priori knowledge and robust in the face of parameter changes. Experimental studies carried out on a direct drive arm are presented, indicating that the proposed approach is a good candidate for trajectory control applications. 相似文献