双旋转机空气动力学系统的反向建模,分析和控制与优化的人工智能控制器
Ahmad Al-Talabi1, Taqwa Oday Fahad2, Aqeel Abdulazeez Mohammed3
1Department of Medical Instrumentation Techniques, College of Engineering and Information Technology, AlShaab University, Baghdad, Iraq.
PloS one
|May 19, 2025
概括
一个新的最佳反向辐射基函数 (RBF) 神经网络控制器增强了双旋转机空气动力系统 (TRAS) 的控制. 这种先进的模型显著提高了比现有方法的性能,提供更快的响应和降低了俯冲和偏航控制的超越.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- 双旋转机空气动力系统 (TRAS) 由于高非线性和跨通道合,存在复杂的多输入多输出 (MIMO) 控制挑战.
- 现有的控制方法,如分数顺序PID (FOPID) 和粒子集群优化-PID (PSO-PID),难以实现这些系统的最佳性能.
研究的目的:
- 提出一种新的最佳逆光径基函数 (RBF) 神经网络模型,以加强对TRAS的控制.
- 将TRAS解为两个单输入单输出 (SISO) 系统 (pitch和yaw) 以进行独立的建模和控制.
- 使用原子搜索优化 (ASO) 算法优化反向RBF神经网络的参数.
主要方法:
- 动态分析和TRAS模型的线性化.
- 将MIMO TRAS解为两个SISO子系统 (斜率和曲率模型).
- 使用反向RBF神经网络建模输出关系,参数由原子搜索优化 (ASO) 优化.
- 实施混合控制器,将比例导数 (PD) 控制与拟议的反向神经模型前控制器相结合.
主要成果:
- 拟议的控制器在TRAS控制中明显优于FOPID和PSO-PID控制器.
- 对于Yaw模型,与FOPID相比,改进包括88.3%更快的上升时间和96.0%更快的沉降时间.
- 对于Pitch模型,改进包括42.8%更快的上升时间和73.9%更快的结算时间,与FOPID相比,在两种模型中都大幅减少了超标.
- 综合性能指数显著下降,表明整体控制效率优越.
结论:
- 新的最佳反向RBF神经网络控制器为控制TRAS等高度非线性和合的MIMO系统提供了强大的和有效的解决方案.
- 优化ASO的反向RBF模型提供了准确的系统表示和有效的前控制,从而大大提高了性能.
- 与传统和先进的基于PID的控制器相比,建议的控制策略显示出优越的动态响应和稳定性.
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