使用人工神经网络调整无人驾驶飞行器的比例-整体-衍生控制参数,以实现点对点轨迹的方法

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Published on: November 26, 2019

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Burak Ulu¹, Sertaç Savaş¹, Ömer Faruk Ergin²

¹Department of Mechatronics Engineering, Erciyes University, 38039 Kayseri, Turkey.

Sensors (Basel, Switzerland)

|May 11, 2024

概括

这项研究引入了一种新的神经网络方法,用于优化微型飞行器 (MAV) 中的比例-积分-导数 (PID) 控制器参数. 这种方法提高了轨迹控制的准确性,特别是在挑战性,受限制的环境中工作的四旋翼,如果园.

科学领域:

机器人技术机器人技术机器人技术
控制系统工程控制系统工程
人工智能的人工智能

背景情况:

对无人驾驶微型飞行器 (MAV) 来说,轨道控制至关重要,特别是在风等严重干扰的环境中.
比例积分导数 (PID) 控制器是轨迹控制的标准,但最佳增益调整对于高精度至关重要.
传统的PID参数调节手动或自动调节方法对于在狭窄的空间 (如狭窄的果园) 操作的MAV是不切实际的.

研究的目的:

开发一种创新的解决方案,用于最优的PID参数调整,专门用于限制环境中的四旋翼MAV.
为了提高MAV在艰难地形上航行的轨迹控制的效率和准确性.
解决现有的PID调方法在实际应用中的局限性,如果园监控.

主要方法:

提出了一种基于神经网络的新方法来调整最佳PID控制参数.
进行了飞行模拟,以评估拟议的神经网络模型的性能.
前后传播网络 (FFBPN) 特别被调查其在轨迹跟踪方面的有效性.

主要成果:

拟议的神经网络方法在模拟中证明了成功的轨迹跟踪性能.
前向向后传播网络 (FFBPN) 显示出卓越的性能,特别是在度跟踪方面.
对于度跟踪,实现了7.52745 × 10-5的平方根平均误差 (RMSE),表明了高精度.

结论:

开发的基于神经网络的PID调方法显著提高了MAV的轨迹控制效率.
这种方法在具有挑战性和受限制的环境中非常有效,为果园中的MAV提供了实际解决方案.
模拟结果验证了高性能和在困难的操作环境下实现自动飞行能力的潜力.

关键词:

农业技术农业技术农业技术自主导航自主导航自主导航神经网络的神经网络的神经网络控制轨道的轨道控制.无人驾驶飞行器无人驾驶飞行器无人驾驶飞行器

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