使用自我组织的内核极端机器学习的非非非非非线性系统的自适应控制
Jyothis Sebastian1, Kavyasree Raj S1, Shihabudheen K V1
1National Institute of Technology, Calicut, NIT Campus P.O., Kozhikode, 673 601, Kerala, India.
ISA transactions
|December 30, 2023
概括
我们介绍了一个自组织的内核极端学习机器 (KELM) 适应控制器,用于非线性系统. 这种新的方法通过动态调整其结构,提高控制性能和稳定性来改进传统的神经网络.
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
- 非线性动力学是一种非线性动力学.
背景情况:
- 传统的神经网络 (NN),如多层前NN和辐射基函数NN (RBFNN),在自适应控制器设计中表现出缓慢的参数融合.
- 极端学习机器 (ELM) 提供更快的训练,但缺乏内核方法的适应性.
- 现有的 NN 结构往往是固定的,这导致在培训期间的网络规模效率低下.
研究的目的:
- 提出一个自组织的内核极端学习机器 (KELM) 适应控制器,用于非affine非线性系统.
- 解决传统NN的缓慢融合问题和固定尺寸网络的结构限制.
- 在非线性系统中提高控制性能,稳定性和稳定性.
主要方法:
- 开发一个自组织的KELM控制器,根据输出重量变化动态调整其结构.
- 使用Lyapunov技术来导出隐藏层参数的更新规律,确保系统稳定性.
- 在工艺和测量噪声下对数值和混乱系统使用轨迹跟踪控制器进行比较分析.
主要成果:
- 拟议的自组织KELM控制器在模拟中表现出比ELM和RBFNN的控制器更好的性能.
- 在旋转伺服系统上的实验验证证证了控制器在实时应用中的有效性.
- 通过参数变化和在伺服系统实验中改变的操作条件来验证强度.
结论:
- 自组织的KELM自适应控制器为控制非非线性系统提供了有效的解决方案.
- 动态结构适应和稳定性保证在现有方法上提供了显著的优势.
- 该控制器对需要强大和适应性控制的真实世界应用具有前景.
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