• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Computing and Information Science in Engineering >Using Q-Learning and Genetic Algorithms to Improve the Efficiency of Weight Adjustments for Optimal Control and Design Problems
【24h】

Using Q-Learning and Genetic Algorithms to Improve the Efficiency of Weight Adjustments for Optimal Control and Design Problems

机译：使用Q学习和遗传算法提高最优控制和设计问题的权重调整效率

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In traditional optimal control and design problems, the control gains and design parameters are usually derived to minimize a cost function reflecting the system performance and control effort. One major challenge of such approaches is the selection of weighting matrices in the cost function, which are usually determined via trial-and-error and human intuition. While various techniques have been proposed to automate the weight selection process, they either can not address complex design problems or suffer from slow convergence rate and high computational costs. We propose a layered approach based on Q-learning, a reinforcement learning technique, on top of genetic algorithms (GA) to determine the best weightings for optimal control and design problems. The layered approach allows for reuse of knowledge. Knowledge obtained via Q-learning in a design problem can be used to speed up the convergence rate of a similar design problem. Moreover, the layered approach allows for solving optimizations that cannot be solved by GA alone. To test the proposed method, we perform numerical experiments on a sample active-passive hybrid vibration control problem, namely adaptive structures with active-passive hybrid piezoelectric networks. These numerical experiments show that the proposed Q-learning scheme is a promising approach for automation of weight selection for complex design problems.
机译：在传统的最佳控制和设计问题中，通常会得出控制增益和设计参数，以最小化反映系统性能和控制工作量的成本函数。这种方法的主要挑战是成本函数中权重矩阵的选择，通常通过反复试验和人的直觉来确定权重矩阵。尽管已经提出了各种技术来使权重选择过程自动化，但是它们要么不能解决复杂的设计问题，要么会导致收敛速度慢和计算成本高。我们在遗传算法（GA）的基础上，提出了一种基于Q学习（一种强化学习技术）的分层方法，以确定针对最佳控制和设计问题的最佳权重。分层方法允许知识的重用。通过Q学习在设计问题中获得的知识可用于加快类似设计问题的收敛速度。此外，分层方法可以解决仅靠GA无法解决的优化问题。为了测试所提出的方法，我们对一个样本主动-被动混合振动控制问题进行了数值实验，即具有主动-被动混合压电网络的自适应结构。这些数值实验表明，所提出的Q学习方案是针对复杂设计问题的权重选择自动化的有前途的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号