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Rosenbrock Methods for Solving Riccati Differential Equations

机译:解决Riccati微分方程的Rosenbrock方法

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The Riccati differential equation (RDE) arises in several fields like optimal control, optimal filtering, ${bf H}_{infty}$ control of linear time-varying systems, differential games, etc. In the literature there is a large variety of approaches to compute its solution. Particularly for stiff RDEs, matrix-valued versions of the standard multi-step methods for solving ordinary differential equations have given good results. In this technical note we discuss a particular class of one-step methods. These are the linear-implicit Runge–Kutta methods or Rosenbrock methods. We show that they offer a practical alternative for solving stiff RDEs. They can be implemented with good stability properties and allow for a cheap step size control. The matrix valued version of the Rosenbrock methods for RDEs requires the solution of one Sylvester equation in each stage of the method. For the case in which the coefficient matrices of the Sylvester equation are dense, the Bartels–Stewart method can be efficiently applied for solving the equations. The computational cost (computing time and memory requirements) is smaller than for multi-step methods.
机译:Riccati微分方程(RDE)出现在多个领域,例如最优控制,最优滤波,线性时变系统的$ {bf H} _ {infty} $控制,微分博弈等。在文献中,存在各种各样的计算其解决方案的方法。特别是对于刚性RDE,用于求解常微分方程的标准多步方法的矩阵值版本已获得良好的结果。在本技术说明中,我们讨论了一类特殊的单步方法。这些是线性隐式Runge–Kutta方法或Rosenbrock方法。我们表明,它们为解决刚性RDE提供了一种实用的选择。它们可以实现具有良好的稳定性,并允许廉价的步长控制。用于RDE的Rosenbrock方法的矩阵值版本需要在该方法的每个阶段求解一个Sylvester方程。对于Sylvester方程的系数矩阵是稠密的情况,可以有效地应用Bartels–Stewart方法求解方程。计算成本(计算时间和内存要求)比多步骤方法小。

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