无人机系统的自适应滑动模式故障耐受控制基于辐射基础功能神经网络的无人机系统
Dehua Zhang1, Lei Meng1, Yao Hao1
1School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China.
Scientific reports
|July 28, 2025
概括
本研究引入了无人机 (UAV) 用自适应滑动模式控制和神经网络的被动故障耐受性控制 (FTC) 系统. 该方法提高了系统对故障和干扰的弹性,以提高性能.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 像无人机这样的复杂系统容易受到故障和外部干扰的性能下降的影响.
- 现有的容错控制 (FTC) 方法通常依赖于故障检测,故障检测可能很慢或不可靠.
- 传统的滑动模式控制 (SMC) 受到声的影响,影响系统可靠性.
研究的目的:
- 开发一种被动的FTC方法,以提高复杂系统的弹性和快速恢复.
- 将辐射基函数 (RBF) 神经网络与SMC集成,以估计不确定性并减轻聊天.
- 改进RBF网络的参数调整,以实现更好的融合和稳定性.
主要方法:
- 设计了一个被动的FTC策略,将SMC与RBF神经网络结合起来.
- 使用RBF网络来动态估计系统不确定性.
- 在RBF参数调整中使用了带有自适应突变 (MPSO) 的增强粒子群优化 (PSO).
- 利亚普诺夫理论被用来保证闭环系统的稳定性.
主要成果:
- MPSO-RBF方法有效估计了系统不确定性,并减少了SMC聊天.
- 与标准PSO相比,MPSO的自适应突变机制提高了汇率和参数稳定性.
- 用四旋翼无人机模型进行的模拟显示了对故障和干扰的优越性能.
- 拟议的FTC方法表现优于传统和标准的适应性SMC基准.
结论:
- 基于MPSO-RBF的自适应滑动模式FTC为提高无人机弹性提供了强大而高效的解决方案.
- 这种被动的FTC方法提供了快速恢复,没有明确的故障检测机制.
- 该研究验证了将高级优化与神经网络集成到复杂控制问题的有效性.
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