HILO-MPC is a Python toolbox for easy, flexible and fast development of machine-learning-supported optimal control and estimation problems
Full Changelog: https://github.com/hilo-mpc/hilo-mpc/compare/v1.0.4...v1.1.0
Solved #16. Now the multi start does not take the results if the solver status is not either "solution found" or "solved to an acceptable level".
Fixes #11
Fixed bug with NMPC box constraints.
In a place there was x_lb
instead of x_ub
.
This is the initial release of HILO-MPC. HILO-MPC is a Python toolbox for easy, flexible, and fast realization of machine-learning-supported optimal control, and estimation problems. It can be used for model predictive control, moving horizon estimation, Kalman filters, solving optimal control problems, and has interfaces to embedded model predictive control tools.
Currently, the following machine learning models are supported:
At the moment the following MPC and optimal control problems can be solved: