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
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Clone the repository
git clone https://github.com/martindoff/Radial-basis-TMPC.git
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In the MATLAB command window, run
convex_anmpc_main