对不确定的非线性系统通过生成对抗网络进行反线性化控制
Nuan Wen1, Zhenghua Liu1, Weihong Wang1
1School of Automation Science and Electrical Engineering, Beihang University, 37 XueYuan Road, Haidian District, Beijing 100191, China.
ISA transactions
|January 3, 2024
概括
本研究介绍了一种使用生成对抗网络 (GAN) 创建不确定非线性系统的反线性化控制器 (FLC) 的新方法,提高了跟踪性能和稳定性.
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:
- 不确定的非线性系统带来了重要的控制挑战.
- 现有的反线性化控制器 (FLC) 与系统不确定性作斗争.
- 生成对抗网络 (GAN) 提供了学习复杂系统动态的潜力.
研究的目的:
- 为使用GAN的不确定非线性系统开发一种学习反线性化控制器 (FLC) 的新方法.
- 为了提高输出输入不确定非线性系统的参考跟踪性能.
- 为学习控制器的融合和稳定提供理论保证.
主要方法:
- 使用生成对抗网络 (GAN) 来估计系统的不确定性.
- 在控制器学习中使用minimax双玩家优化框架.
- 确保发电机结构严格凸起,并增强对抗损失,以减轻GANs模式崩.
- 通过全面的模拟和实践实验验证该方法.
主要成果:
- 拟议的基于GAN的FLC显著提高了不确定的非线性系统中的参考跟踪性能.
- 建立了强大的FLC对收和稳定的理论保证.
- 该方法有效地解决了GAN培训中常见的模式崩挑战.
- 实验结果证实了拟议方法的优越性和有效性.
结论:
- 生成性对抗性网络提供了一个强大的框架,用于学习强大的反对不确定非线性系统的线性化控制器.
- 开发的方法提供了增强的性能和稳定性保证,克服了传统方法的局限性.
- 这项工作表明了在先进的控制系统设计中应用先进的人工智能技术的有希望的方向.
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