Computational Science Asked on July 22, 2020

I need to use a Discrete-time Algebraic Riccati Equation (DARE) solver for an embedded controller (with limited processing power) in a research project and sadly, I can’t find any implementation of it in C++ that I can understand. I use C++ since it is faster than higher level languages. I found various libraries online that offer Riccati equation or LQR solvers such as the Control Toolbox (https://adrlab.bitbucket.io/ct/v2.3/ct_doc/doc/html/index.html) or Drake (https://drake.mit.edu/) but I can’t understand the language and I think they are full of unnecessary functions and things for the simple job of solving DARE. I would like the implementation to use basic open source C++ libraries or header files such as Eigen, Armadillo etc. with a lot of documentation, tutorials, or an active online community using them so that I can learn the syntax on my own. I’m looking for a simple code like Arash’s C++ implementation of the Continuous-time Algebraic Riccati Equation (CARE) solver: https://math.stackexchange.com/questions/679989/analytic-solution-to-structured-algebraic-riccati-equation .

To anyone who can help me, I would like to cite you in the bibliography so you can please include a “How to Cite” section. If you don’t have a personal C++ code, can you please refer me to a library or something that can help me implement DARE in C++? I attempted to write my own solver but when I read papers of the Riccati solver, I was dumbfounded by the math and terminologies since I am an undergraduate engineering student with only basic knowledge in linear algebra. I’m not sure if this is the right site to ask but I can’t find anything else so please help me out.

It is an old question but I will answer it. Here is a good library: https://github.com/TakaHoribe/Riccati_Solver

Answered by Bruno Henrique Peixoto on July 22, 2020

If you want a ten-line solution that is decently fast and stable, you can implement yourself the structured doubling algorithm: set up the coupled iteration

$A_0 = A, G_0 = G = BR^{-1}B^T, H_0 = Q$

While $frac{|H_{k+1}-H_k|}{|H_{k+1}|} geq varepsilon$:

$quad quad A_{k+1} = A_k(I+G_kH_k)^{-1}A_k$

$quad quad G_{k+1} = G_k + A_k(I+G_kH_k)^{-1}G_kA_k^T$

$quad quad H_{k+1} = H_k + A_k^TH_k(I+G_kH_k)^{-1}A_k$

Return $H_{k+1}$.

The matrices $H_k$ are symmetric, and they converge quadratically fast to the stabilizing solution $X$ of $X = A^TXA - (B^TXA)^T(R+B^TXB)^{-1}B^TXA + Q$.

Reference: http://dx.doi.org/10.1080/00207170410001714988 .

Answered by Federico Poloni on July 22, 2020

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