不知动力学非线性系统的最佳稳定控制通过NN学习与放松激发
Rui Luo1, Zhinan Peng1,2, Jiangping Hu1,3
1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Entropy (Basel, Switzerland)
|January 22, 2024
概括
本研究介绍了一种适应神经网络控制系统,用于面临未知的动态和干扰的非线性系统. 这种新的方法增强了参数估计,使得可靠的控制与放松的条件,以提高性能.
科学领域:
- 控制理论 控制理论
- 机器学习 机器学习
- 非线性系统是非线性系统.
背景情况:
- 对于具有不确定性的非线性系统来说,最佳的稳定控制是至关重要的.
- 现有的神经网络 (NN) 方法在有限激发下面临参数趋同的挑战.
- 接近未知的系统动态和干扰是一个关键问题.
研究的目的:
- 开发使用NN的自适应性学习结构,以实现非线性连续时间系统的最佳可靠控制.
- 有效地应对未知的系统动态和外部干扰.
- 在识别器关键的NN学习控制中放松融合条件.
主要方法:
- 使用一个系统标识符的NNs和参数估计来近似未知的系统矩阵和干扰.
- 采用一个批评学习控制结构来计算一个近似的最佳强大的控制器.
- 设计基于Kreisselmeier的回归器扩展和混合技术的新型自适应调整规律,用于NN参数估计.
- 进行理论分析以证明放松的趋同条件.
主要成果:
- 提出的方法成功地近似了未知的系统动态和干扰.
- 新的自适应调整规律显著放松了NN参数估计的激发条件的持久性.
- 理论分析证实了趋同条件的放松.
- 模拟研究证明了自适应式学习方法的有效性.
结论:
- 开发的自适应式学习结构为非线性系统的最佳稳定控制提供了有效的解决方案.
- 新的调整法通过放松趋同要求,提供了显著的进步.
- 该方法证明了其实际适用性和在处理系统不确定性方面提高了性能.
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