Release of OpenOCL v4.33


Release of OpenOCL v4.33 Getting started with OpenOCL is now easier than ever. Just download the .mltbx package and install it as a Matlab Add-in. OpenOCL is then automatically setup with your Matlab path. You don’t need to download any dependencies as CasADi will be downloaded automatically at the first run of OpenOCL. You can also install OpenOCL by browsing Matlab Add-Ons or at File Exchange. We have changed the license from v4.29 onwards to...

Modeling for Reinforcement Learning and Optimal Control: Double pendulum on a cart


Modeling for Reinforcement Learning and Optimal Control: Double pendulum on a cart Modeling is an integral part of engineering and probably any other domain. With the popularity of machine learning a new type of black box model in form of artificial neural networks is on the way of replacing in parts models of the traditional approaches. However we think that this does not mean that traditional models will be less significant, but they might get...

Release of OpenOCL v4.20


Release of OpenOCL v4.20 With the release of OpenOCL 4.20 the non-linear program which is constructed from the optimal control problem definition now has a block sparse structure. This should enable the use of better structure exploiting solvers. Ipopt’s performance might improve as well. Parameters will not destroy the block sparsity structure. Get the new release here.

Release of OpenOCL v4.00


Release of OpenOCL v4.00 We release OpenOCL 4.00 which from now on only support one way to define a dynamical system and optimal control problem. The old style of inheriting from OclSystem and OclOCP is not supported anymore. Get the new version of the toolbox here. The examples and documentation were updated accordingly. You now define a System as e.g. system = OclSystem(@varsfun, @eqfun); you can also use a keyword/named parameter list (in arbitrary order)...

[Contributed] 1:10 scale pro stock remote car racing


1:10 scale pro stock remote car racing Mustafa Alp from the Polytechnic University of Milan sent us a great video of his implementation with the OpenOCL toolbox. (Image courtesy of Mustafa Alp) Here are some details on the implementation: The vehicle model resembles a car of the 1:10 scale pro stock division remote controlled racing. The track length is 337 meters. The simulated laptime of 22.08 seconds is close to the laptime that is achieved...

Minor release OpenOCL v3.20


Minor release OpenOCL v3.20 Minor release OpenOCL 3.20 with a fix for the Simulator. Download the toolbox from here. In the upcoming weeks we are expecting a major release where the backend of OpenOCL is implemented in C++, and following that OpenOCL will become a Python interface.

New release OpenOCL v3.11


New release OpenOCL v3.11 We are happy to announce that OpenOCL v3 is ready and released. It contains many improvements with respect to the previous version and also some API changes. If you come from an earlier version see the release notes that contains a list of changes below. Otherwise you can directly go to the download page of OpenOCL v3.11 or pull the latest version from the master branch. Also note that the URL...

API docs are now on the website!

API documentation is now on the website! Go to api-docs to view all classes, methods, and functions that you need to implement optimal control problems with the OpenOCL toolbox. The API should be almost complete for all public functions. It will be gradually extended as the toolbox evolves. More updates including the release of OpenOCL v3.0 with a few new features and an improved website with more code examples are coming in the next days!...