适应性模糊神经超扭转控制微陀螺仪传感器的微陀螺仪传感器
1College of Artificial Intelligence and Automation, Jiangsu Key Lab. of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou, 213200, China.
Scientific reports
|October 31, 2024
概括
使用模糊神经网络的自适应性超扭转滑动模式控制 (STSMC) 稳定了微陀螺仪. 这种先进的控制方法有效地减少了用于精确振动维护的跟踪错误.
科学领域:
- 控制系统工程 控制系统工程
- 机器人技术 机器人技术 机器人技术
- 机械电子学是什么意思 机械电子学
背景情况:
- 微陀螺仪需要精确的振动控制来进行准确的测量.
- 传统的滑动模式控制受到系统聊天的影响.
- 非线性系统的不确定性在陀螺仪控制中带来了挑战.
研究的目的:
- 为微陀螺仪设计一个自适应式超扭转滑动模式控制 (STSMC).
- 为了增强系统的稳定性并尽量减少跟踪错误,使用双循环递归模糊神经网络 (TLRFNN).
- 解决在线参数估计和系统不确定性的问题.
主要方法:
- 利用基于参数线性化的自适应技术,在线估计未知非线性系统参数.
- 实施STSMC以克服常规滑动模式控制中固有的聊天问题.
- 采用双循环递归模糊神经网络 (TLRFNN) 来近似系统不确定性并存储先前的信息.
- 在Lyapunov稳定性框架内推导适应性规律以确保系统稳定性.
主要成果:
- 拟议的STSMC系统在微陀螺仪中显示出良好的跟踪性能.
- 该系统有效地保持陀螺仪的振动.
- 在x和y方向分别实现了[公式:见文本]和[公式:见文本]的根平均平方误差 (RMSE),表示最小的跟踪误差.
结论:
- 基于TLRFNN的自适应STSMC为微陀螺仪提供了强大而稳定的控制解决方案.
- 该方法成功地减轻了聊天,并有效地处理系统的不确定性.
- 模拟结果验证了拟议的控制系统对于精确的振动维护和精确的跟踪的能力.
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