Approximate Robust NMPC using Reinforcement Learning
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 21 references that are in the map. First posted on arXiv 6 Apr 2021.
Builds on
10 of 21 references.
References · 10
- 2018Learning-based Model Predictive Control for Safe Explorationcited by 116
- 2018Efficient representation and approximation of model predictive control laws via deep learningcited by 55
- 2019Data-driven Economic NMPC using Reinforcement Learningcited by 55
- 2019Practical Reinforcement Learning of Stabilizing Economic MPCcited by 15
- 2017Cautious NMPC with Gaussian Process Dynamics for Autonomous Miniature Race Carscited by 13
- 2020Reinforcement Learning for Mixed-Integer Problems Based on MPCcited by 11
- 2021Reinforcement Learning based on Scenario-tree MPC for ASVscited by 7
- 2021Reinforcement Learning based on MPC/MHE for Unmodeled and Partially Observable Dynamicscited by 5
- 2016Robust MPC via Min-Max Differential Inequalitiescited by 4
- 2019Parallel Explicit Tube Model Predictive Controlcited by 1
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Citing papers per year: 2023: 1.
1 paper.