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Control Barrier Functions (CBF) have been recently utilized in the design of provably safe feedback control laws for nonlinear systems.
Neural networks for control systems - a survey
Kenneth J. Hunt, Daniel G. Sbarbaro, Rafat Zbikowski, and Peter Gawthrop · 1992
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Set invariance in control
Franco Blanchini · 1999
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An introduction to the use of neural networks in control systems
Martin T. Hagan, Howard B. Demuth, and Orlando De Jesus · 2002
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
Earlier work this paper cites.
Boosting structured prediction for imitation learning
JA Bagnell, Joel Chestnutt, David M Bradley, and Nathan D Ratliff · 2007
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Applications of Neural Networks in High Assurance Systems , volume 268 of SCI
Johann Schumann and Yan Liu · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Control barrier function based quadratic programs with application to adaptive cruise control
Aaron D Ames, Jessy W Grizzle, and Paulo Tabuada · 2014
Earlier work this paper cites.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
Cited alongside, same era.
Exponential control barrier functions for enforcing high relative-degree safety-critical constraints
Quan Nguyen and Koushil Sreenath · 2016
Cited alongside, same era.
Nonsmooth barrier functions with applications to multi-robot systems
Paul Glotfelter, Jorge Cortés, and Magnus Egerstedt · 2017
Cited alongside, same era.
Approximating explicit model predictive control using constrained neural networks
Steven Chen, Kelsey Saulnier, Nikolay Atanasov, Daniel D Lee, Vijay Kumar, George J Pappas, and Manfred Morari · 2018
Cited alongside, same era.
Learning and verification of feedback control systems using feedforward neural networks
Souradeep Dutta, Susmit Jha, Sriram Sankaranarayanan, and Ashish Tiwari · 2018
Cited alongside, same era.
Input-to-state safety with control barrier functions
Control barrier functions: Theory and applications
Aaron D Ames, Samuel Coogan, Magnus Egerstedt, Gennaro Notomista, Koushil Sreenath, and Paulo Tabuada · 2019
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Trajectory tracking control for robotic vehicles using counterexample guided training of neural networks
Arthur Claviere, Souradeep Dutta, and Sriram Sankaranarayanan · 2019
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Sherlock - a tool for verification of neural network feedback systems: Demo abstract
Souradeep Dutta, Xin Chen, Susmit Jha, Sriram Sankaranarayanan, and Ashish Tiwari · 2019
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Control barrier functions for multi-agent systems under conflicting local signal temporal logic tasks
Lars Lindemann and Dimos V Dimarogonas · 2019
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Sqil: Imitation learning via regularized behavioral cloning
Siddharth Reddy, Anca D Dragan, and Sergey Levine · 2019
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Shishir Kolathaya and Aaron D Ames · 2018
Cited alongside, same era.
Multi-agent generative adversarial imitation learning
Jiaming Song, Hongyu Ren, Dorsa Sadigh, and Stefano Ermon · 2018
Cited alongside, same era.
Reasoning about safety of learning-enabled components in autonomous cyber-physical systems
C. E. Tuncali, H. Ito, J. Kapinski, and J. V. Deshmukh · 2018
Cited alongside, same era.
Gray-box adversarial testing for control systems with machine learning components
Shakiba Yaghoubi and Georgios Fainekos
Cited in the paper.
Worst-case satisfaction of stl specifications using feedforward neural network controllers: a lagrange multipliers approach
Shakiba Yaghoubi and Georgios Fainekos
Cited in the paper.
Control barrier functions for systems with high relative degree
Wei Xiao and Calin Belta · 2019
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Sampling-based motion planning via control barrier functions
Guang Yang, Bee Vang, Zachary Serlin, Calin Belta, and Roberto Tron · 2019
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Near-optimal rapid mpc using neural networks: A primal-dual policy learning framework
Xiaojing Zhang, Monimoy Bujarbaruah, and Francesco Borrelli · 2019
Later among the works it cites.