Learning-based robust tube-based MPC of nonlinear systems via difference of convex radial basis functions approximations.

Difference-of-convex-functions (DC) decomposition of system dynamics via radial basis functions (RBF) approximations.

Learning-based robust tube-based MPC of dynamic systems approximated by difference-of-convex (DC) radial basis functions (RBF) models. Successive linearisations of the learned dynamics in DC form are performed to express the MPC scheme as a sequence of convex programs. Convexity in the learned dynamics is exploited to bound successive linearisation errors tightly and treat them as bounded disturbances in a robust MPC scheme. Application to the coupled tank problem. This novel computationally tractabe tube-based MPC algorithm is presented in the paper "Safe Learning in Nonlinear Model Predictive Control" by Johannes Buerger, Mark Cannon and Martin Doff-Sotta. It is an extension of our previous work here and here

• MATLAB
• CVX
• MOSEK

1. Clone the repository

   git clone https://github.com/martindoff/Radial-basis-TMPC.git

2. In the MATLAB command window, run

   convex_anmpc_main