使用综合神经网络和模糊逻辑的无人机混合适应PID控制策略
1Information Network Security Administration (INSA), Aerospace Division, Addis Ababa, Ethiopia.
PloS one
|August 29, 2025
概括
本研究介绍了一种混合控制策略,该策略结合了神经网络和模糊逻辑,用于无人机系统. 这种方法比独立方法更有效地优化无人机动力和轨迹跟踪.
科学领域:
- 机器人和控制系统
- 工程中的人工智能
- 航空航天工程
背景情况:
- 无人驾驶飞行器 (UAV) 需要复杂的控制系统来稳定和精确运行.
- 传统的比例整合导数 (PID) 控制器在优化复杂的多维动态方面经常面临限制.
- 独立神经网络 (NNPID) 和模糊逻辑 (FPID) 方法已经显示出承诺,但在全面的无人机控制方面存在局限性.
研究的目的:
- 开发和评估一种新的混合控制策略,将神经网络和模糊逻辑集成到无人机中.
- 优化PID控制器收益的调整,以提高无人机动态和轨迹跟踪.
- 证明混合NNPID+FPID方法比单个NN和模糊逻辑方法的性能优越.
主要方法:
- 一个混合控制策略 (NNPID+FPID) 结合了神经网络和模糊逻辑.
- 神经网络被用来微调 y 和 ψ 状态的 PID 增益,重量通过梯度下降进行更新.
- 采用模糊逻辑来使用启发式规则和成员函数动态调整x,z,φ和θ状态的PID增益.
主要成果:
- 混合NNPID+FPID控制策略显著提高了无人机系统的轨迹跟踪性能.
- 与独立的NNPID和FPID方法相比,拟的方法显示了整体无人机控制效率的提高.
- 混合系统的适应性,利用神经网络适应性和模糊逻辑启发规则, 证明有效.
结论:
- 神经网络和模糊逻辑的结合提供了一个强大的方法来解决无人机的多维控制挑战.
- 该NNPID+FPID战略为优化无人机飞行动态和控制提供了强大的适应性解决方案.
- 这项研究证实了混合智能控制系统对先进的飞行器应用的协同效益.
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