Robust MPC via Min-Max Differential Inequalities
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Abstract (arXiv)
This paper is concerned with tube-based model predictive control (MPC) for both linear and nonlinear, input-affine continuous-time dynamic systems that are affected by time-varying disturbances. We derive a min-max differential inequality describing the support function of positive robust forward invariant tubes, which can be used to construct a variety of tube-based model predictive controllers. These constructions are conservative, but computationally tractable and their complexity scales linearly with the length of the prediction horizon. In contrast to many existing tube-based MPC implementations, the proposed framework does not involve discretizing the control policy and, therefore, the conservatism of the predicted tube depends solely on the accuracy of the set parameterization. The proposed approach is then used to construct a robust MPC scheme based on tubes with ellipsoidal cross-sections. This ellipsoidal MPC scheme is based on solving an optimal control problem under linear matrix inequality constraints. We illustrate these results with the numerical case study of a spring-mass-damper system.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Nov 2016.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2023The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systemscited by 88
- 2025Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classificationcited by 26
- 2021Approximate Robust NMPC using Reinforcement Learningcited by 2
- 2026GPU-Parallel Linearization Error Bounds for Real-Time Robust Optimal Control of Nonlinear and Neural Network Dynamicscited by 0
- 2026GPU-Parallel Linearization Error Bounds for Real-Time Robust Optimal Control of Nonlinear and Neural Network Dynamics1 Jul 2026
- 2025Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification4 Feb 2025
- 2023The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems11 Sep 2023
- 2021Approximate Robust NMPC using Reinforcement Learning6 Apr 2021