The use of Riccati equations in optimal control theory

AI Thread Summary
The discussion focuses on the formulation of linear control theory using matrix Riccati equations, specifically the equations ##\dot{x}=Ax+Bu## and ##\dot{u}=Cx+Du##. It highlights that rewriting these equations as a matrix differential equation allows for easier solutions through diagonalization techniques. The advantage of this approach lies in its accessibility to useful mathematical methods for solving control problems. A participant shares a review paper that may provide additional insights into the topic. Overall, the use of Riccati equations in optimal control theory enhances problem-solving efficiency.
John Finn
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I know that linear control theory, in the form ##\dot{x}=Ax+Bu##, ##\dot{u}=Cx+Du##, can be put in the form of a matrix Riccati equation. But is there really an advantage to doing so?
 
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John Finn said:
I know that linear control theory, in the form ##\dot{x}=Ax+Bu##, ##\dot{u}=Cx+Du##, can be put in the form of a matrix Riccati equation. But is there really an advantage to doing so?
I don't know anything about linear control theory or matrix Riccati equations, but the above looks like linear algebra as it relates to systems of differential equations, which I do know something about.
Assuming A, B, C, and D are constants, the two equations above can be rewritten in this form:
##\begin{bmatrix}\dot x \\ \dot u \end{bmatrix} = \begin{bmatrix}A & B \\ C & D \end{bmatrix}\begin{bmatrix} x \\ u \end{bmatrix}##

The advantage of writing the system in this form is that this matrix differential equation can be solved for x and u by diagonalizing the 2 x 2 matrix I wrote using standard techniques.
 
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