• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International conference on optimization: Techniques and Applications >Filtering Solution of Nonlinear Stochastic Optimal Control Problem in Discrete-Time with Model-Reality Differences
【24h】

Filtering Solution of Nonlinear Stochastic Optimal Control Problem in Discrete-Time with Model-Reality Differences

机译：不同时间与模型 - 现实差异的离散时间过滤非线性随机最佳控制问题的解

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper we propose an efficient approach for solving the nonlinear stochastic optimal control problem in discrete-time. The aim is to obtain the filtering solution only by solving the linear model-based optimal control problem iteratively. To do this, a mathematical model, which is a simplified model-based optimal control problem derived from the original optimal control problem, is constructed. It can be seen that this simplified mathematical model is a modified linear quadratic Gaussian optimal control model, where the adjusted parameters are added into the model, aiming to approximate the solution of the original optimal control problem iteratively. Before the iteration begins, the adjusted parameters are zero value and the model is actually a standard linear quadratic Gaussian optimal control model. It is important to note that the reality information is employed in estimating the dynamic of state, where the output from the real plant is measured. During the iterative procedure, the differences among the real plant and the model used are captured by the adjusted parameters and the modified linear quadratic Gaussian optimal control model is optimized with these specified adjusted parameters, in turn, to update their values based on the measured output iteratively. Therefore, the interaction on system optimization and parameter estimation is generated. When the convergence is achieving, the true filtering solution of the original optimal control problem is obtained in spite of model-reality differences. To illustrate the applicable of the proposed algorithm, a problem of nonlinear continuous stirred tank reactor is studied. In conclusion, the efficiency of the proposed algorithm has been shown.
机译：本文提出了一种有效的方法，用于在离散时间内解决非线性随机最佳控制问题。目的是仅通过迭代地解决基于线性模型的最佳控制问题来获得滤波解决方案。为此，构造了一种数学模型，该数学模型是从原始最佳控制问题导出的简化模型的最佳控制问题。可以看出，这种简化的数学模型是修改的线性二次高斯最佳控制模型，其中调整后的参数被添加到模型中，旨在迭代地近似原始最佳控制问题的解决方案。在迭代开始之前，调整后的参数是零值，并且模型实际上是标准的线性二次高斯最佳控制模型。重要的是要注意，现实信息在估计状态的动态时，测量来自真实工厂的输出。在迭代程序期间，通过调整的参数捕获实际工厂和所用模型的差异，并通过这些指定的调整参数进行了优化的改进的线性二次高斯最佳控制模型，反过来，根据测量的输出更新其值迭代地。因此，生成系统优化和参数估计的交互。当收敛正在实现时，尽管有模型 - 现实差异，获得了原始最佳控制问题的真正滤波解决方案。为了说明所提出的算法的适用，研究了非线性连续搅拌釜反应器的问题。总之，已示出所提出的算法的效率。

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