If you are interested in (fast) Model Predictive Control, use MATLAB and like free stuff, jMPC could be for you. See the jMPC Wiki for details on building, tuning and deploying fast model predictive controllers using MATLAB!
The best way to get jMPC is to clone the master branch from GitHub (this page!) using git. I like GitKraken which makes it easy to use git without the command line. Once you have git/GitKraken/some other git client, simply click Clone or download (above, green button) and copy the address of this repository, and clone it to your computer. This way each time I push updates to the master branch, you can simply pull them down without having to redownload the entire jMPC package again!
If you don't use git, and don't want to learn (remember its easy and you should use it!), simply click Clone or download and click Download ZIP and save jMPC somewhere you can find it.
Once you have cloned or downloaded jMPC to your PC, simply open MATLAB and navigate in MATLAB to the directory where you have placed jMPC (hopefully somewhere permanent). Right click on jMPC_Install.m and click Run. Follow the prompts (Yes 'Y' is the normal response to every question) to install jMPC - easy!
The best way to start using jMPC is to have a look at the Examples on the jMPC Wiki. There are lots of problem and code examples (which all work) as well as helpful definitions.
jMPC is released under the 3-clause BSD license, as detailed here. It is free software, and is released as an open-source package.