Tutorial-on-CasADi-with-CPP

This is a Tutorial on how to use CasADi with CPP.

This repo has been tested with:

• GCC 11.1.0, CMake 3.16.3, Ubuntu 20.04.5 LTS

I recommend going through the CasADi's official documentation first, then come to this repo to learn about how to use CasADi with C++. Even though the offical documentation focuses on CasADi's usage in Python and MATLAB, there are a lot of concepts you can borrow from them when coding CasADi in C++.

Dependency

• ipopt
• CasADi

The instructions for dependency installation is shown below.

For Linux

$ sudo apt update
$ sudo apt install build-essential
$ sudo apt install coinor-libipopt-dev
$ pip3 install casadi

Instructions on upgrade GCC to Version 11

$ sudo apt install manpages-dev software-properties-common
$ sudo add-apt-repository ppa:ubuntu-toolchain-r/test
$ sudo apt update && sudo apt install gcc-11 g++-11
$ sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 110
$ sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-11 110

To check GCC's version

$ gcc --version
$ g++ --version

Build

To build casadi from source, see instructions here.

$ sudo apt install gfortran liblapack-dev pkg-config --install-recommends
$ sudo apt install swig
$ cd
$ git clone https://github.com/casadi/casadi.git -b master casadi
$ cd casadi
$ mkdir build
$ cd build
$ cmake -DWITH_PYTHON=ON -DWITH_IPOPT=ON -DWITH_OPENMP=ON -DWITH_THREAD=ON ..
$ make
$ sudo make install

To build this repo,

$ cd
$ git clone https://github.com/zehuilu/Tutorial-on-CasADi-with-CPP.git
$ cd <MAIN_DIRECTORY>
$ mkdir build code_gen
$ cd build
$ cmake ..
$ make

Example

1. See how CasADi and IPOPT work together to solve a race car example,

$ cd <MAIN_DIRECTORY>/build
$ ./example_racecar

2. See some basic operations in CasADi,

$ cd <MAIN_DIRECTORY>/build
$ ./example_basic_operations

3. See how to automatically generate functions in C code, and how to use them in another C++ file.

$ cd <MAIN_DIRECTORY>/build
$ ./example_use_code_gen

4. To solve a Model Predictive Control (MPC) problem or an Cptimal Control problem at run-time, the initial condition changes at each time instance. The most proper way to solve it with CasADi is shown below. Basically, we first formulate a nonlinear programing with some parameters. In this case, the initial condition can be the parameters. Then compile this problem as a shared library. At run-time, revise the parameter value and re-solve it.

First, compile the nonlinear programming

$ cd <MAIN_DIRECTORY>/build
$ ./example_nlp_code_gen

Second, run a solver instance with external shared library

$ cd <MAIN_DIRECTORY>/build
$ ./example_use_nlp_external

To add parameter into a nonlinear programming, and revise the value at run-time, I will add another example later. Oct. 06, 2022

If you want to know how to evoke other operations or functions via CasADi in C++, feel free to post your questions/requests in Issues or Discussions.