神经网络反向控制OWC波浪能量系统的控制
Priyanka Nath1, Sunil Kumar Mishra1, Amitkumar Vidyakant Jha1
1School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India.
Scientific reports
|March 7, 2025
概括
神经网络后退控制 (NN-BSC) 通过改进旋转速度控制来增强振荡水柱 (OWC) 系统. NN-BSC显著优于传统方法,特别是在执行器干扰下,确保更强大的波能量转换.
科学领域:
- 可再生能源系统可再生能源系统
- 控制工程 控制工程 控制工程
- 人工智能的人工智能
背景情况:
- 振荡水柱 (OWC) 波浪能源系统面临由于非线性和不规则波浪条件的控制挑战.
- 传统的控制方法,如PI和倒退控制 (BSC),与系统干扰和非线性动态作斗争.
- 需要强大的和适应性的控制策略来优化OWC系统的性能和效率.
研究的目的:
- 研究神经网络后退控制 (NN-BSC) 的应用,以提高OWC系统的旋转速度控制.
- 解决传统控制方法在处理非线性和执行器干扰方面的局限性.
- 在动态操作条件下提高OWC系统的稳定性和性能.
主要方法:
- 一个切比舍夫神经网络 (NN) 被集成到后退控制 (BSC) 框架中,以创建NN-BSC.
- 使用利亚普诺夫分析验证了NN-BSC控制的OWC系统的稳定性.
- 在MATLAB/SIMULINK的数值模拟中,NN-BSC与不受控制的,PI和传统的BSC系统进行了比较,参数通过粒子优化 (PSO) 进行了优化.
主要成果:
- 与PI和BSC相比,NN-BSC在旋转速度跟踪方面表现出卓越的性能,表现出更顺的控制.
- 在执行器干扰下,NN-BSC的整方位误差 (ISE) 达到了22.5433,相比PI (40.6381) 和BSC (37.1192) 低得多.
- 在干扰场景中,NN-BSC还显示了最小的最大峰值超越 (0.9651rad/s) 和最快的沉降时间 (0.0561s).
结论:
- 拟议的NN-BSC有效地管理非线性,并提高OWC系统的稳定性,特别是在执行器干扰下.
- 与传统的PI和BSC控制策略相比,NN-BSC为OWC旋转速度控制提供了显著的改进.
- 这种先进的控制方法有望实现更高效,更可靠的波浪能量转换.
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