ACADO for MATLAB is a MATLAB interface for the ACADO Toolkit. It brings the ACADO Integrators and algorithms for direct optimal control, model predictive control and parameter estimation to MATLAB. ACADO for MATLAB uses the ACADO Toolkit C++ code base and implements methods to communicate with this code base.
ACADO Toolkit is a software environment and algorithm collection for automatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct optimal control, including model predictive control, state and parameter estimation and robust optimization. The object-oriented design allows for convenient coupling of existing optimization packages and for extending it with user-written optimization routines.
To install and use the Matlab interface you need to have a recent Matlab version and a C++ compiler installed. Follow these steps to get you started in a few minutes.
Three available interfaces:
1. Use the stand-alone Runge-Kutta and BDF's integrators in MATLAB
2. Compose your optimization problem in a MATLAB environment with familiar MATLAB syntax using the generic optimal control interface
3. Write your own C++ code as a MEX-function and compile it using ACADO for MATLAB build-in MEX-compiler
Link your models to ACADO:
1. Link MATLAB ODE or DAE models
2. Link C++ ODE or DAE models
3. Provide optional Jacobians for faster calculations
The folder \<ACADOtoolkit-inst-dir>/interfaces/matlab/examples contain many examples explaining how to use the interface. NOTE: do not use the C++ examples you find on the ACADO website in Matlab, they will not always work although the syntax is very similar.