通过识别器关键神经网络近似与放松的PE条件来进行亲属非线性系统的自适应性最佳控制
Rui Luo1, Zhinan Peng1, Jiangping Hu2
1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
概括
本研究介绍了一种识别器关键 (IC) 学习框架,用于对具有未知动态的非线性系统进行最佳控制. 该方法有效地估计系统动态,并解决最佳控制问题,确保系统稳定性.
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:
- 由于未知的动态,对非线性系统的最佳控制具有挑战性.
- 传统方法通常需要精确的系统模型.
- 适应性控制策略对于现实世界的应用是必要的.
研究的目的:
- 提出一种新的标识符-关键 (IC) 学习框架,以优化对具有未知动态的亲属非线性系统的控制.
- 开发一种不需要先前了解系统参数的适应性最佳控制算法.
- 确保拟议的控制战略的稳定性和有效性.
主要方法:
- 神经网络标识符可以估计未知的系统动态.
- 一个批评神经网络解决了相关的哈密尔顿-雅各比-贝尔曼方程.
- 动态回归器扩展和混合技术用于重量更新规律.
- 利亚普诺夫函数方法分析参数估计和闭环稳定性.
主要成果:
- 拟议的IC学习框架成功地估计了未知的系统动态.
- 适应性最佳控制算法确保了闭环系统的稳定性.
- 数字模拟验证了基于IC学习的最佳控制的有效性.
结论:
- 标识符-关键学习框架提供了一个有效的解决方案,用于适应性最优控制未知动态的亲缘非线性系统.
- 该方法放松了激发条件的持久性,使其更实用.
- 该方法证明了强大的性能和稳定性保证.
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