Approximate Robust NMPC using Reinforcement Learning
This work addresses robust control for nonlinear systems like mobile robots, but it is incremental as it combines existing RL and MPC methods with an ellipsoidal approximation.
The paper tackled controlling nonlinear systems under disturbances by developing a Reinforcement Learning-based Robust Nonlinear Model Predictive Control framework, resulting in improved closed-loop performance tested on a simulated Wheeled Mobile Robot for trajectory tracking and obstacle avoidance.
We present a Reinforcement Learning-based Robust Nonlinear Model Predictive Control (RL-RNMPC) framework for controlling nonlinear systems in the presence of disturbances and uncertainties. An approximate Robust Nonlinear Model Predictive Control (RNMPC) of low computational complexity is used in which the state trajectory uncertainty is modelled via ellipsoids. Reinforcement Learning is then used in order to handle the ellipsoidal approximation and improve the closed-loop performance of the scheme by adjusting the MPC parameters generating the ellipsoids. The approach is tested on a simulated Wheeled Mobile Robot (WMR) tracking a desired trajectory while avoiding static obstacles.