Provably safe and robust learning-based model predictive control
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TLDR
A learning-based model predictive control scheme that provides deterministic guarantees on robustness, while statistical identification tools are used to identify richer models of the system in order to improve performance.About:
This article is published in Automatica.The article was published on 2013-05-01 and is currently open access. It has received 483 citations till now. The article focuses on the topics: Model predictive control & Robustness (computer science).read more
Citations
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Proceedings Article
Safe Model-based Reinforcement Learning with Stability Guarantees
TL;DR: In this paper, the authors present a learning algorithm that explicitly considers safety, defined in terms of stability guarantees, and show how to use statistical models of the dynamics to obtain high-performance control policies with provable stability certificates.
Journal ArticleDOI
Data-Driven Model Predictive Control With Stability and Robustness Guarantees
TL;DR: The presented results provide the first (theoretical) analysis of closed-loop properties, resulting from a simple, purely data-driven MPC scheme, including a slack variable with regularization in the cost.
Journal ArticleDOI
A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems
Jaime F. Fisac,Anayo K. Akametalu,Melanie N. Zeilinger,Shahab Kaynama,Jeremy H. Gillula,Claire J. Tomlin +5 more
TL;DR: A general safety framework based on Hamilton–Jacobi reachability methods that can work in conjunction with an arbitrary learning algorithm is proposed, which proves theoretical safety guarantees combining probabilistic and worst-case analysis and demonstrates the proposed framework experimentally on a quadrotor vehicle.
Proceedings ArticleDOI
Reachability-based safe learning with Gaussian processes
Anayo K. Akametalu,Shahab Kaynama,Jaime F. Fisac,Melanie N. Zeilinger,Jeremy H. Gillula,Claire J. Tomlin +5 more
TL;DR: This work proposes a novel method that uses a principled approach to learn the system's unknown dynamics based on a Gaussian process model and iteratively approximates the maximal safe set and further incorporates safety into the reinforcement learning performance metric, allowing a better integration of safety and learning.
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Survey Constrained model predictive control: Stability and optimality
TL;DR: This review focuses on model predictive control of constrained systems, both linear and nonlinear, and distill from an extensive literature essential principles that ensure stability to present a concise characterization of most of the model predictive controllers that have been proposed in the literature.