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Relevant bibliographies by topics / Verification of control systems

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Journal articles

Academic literature on the topic 'Verification of control systems'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Verification of control systems"

1

Chen, Mo, and Claire J. Tomlin. "Hamilton–Jacobi Reachability: Some Recent Theoretical Advances and Applications in Unmanned Airspace Management." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (2018): 333–58. http://dx.doi.org/10.1146/annurev-control-060117-104941.

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Autonomous systems are becoming pervasive in everyday life, and many of these systems are complex and safety-critical. Formal verification is important for providing performance and safety guarantees for these systems. In particular, Hamilton–Jacobi (HJ) reachability is a formal verification tool for nonlinear and hybrid systems; however, it is computationally intractable for analyzing complex systems, and computational burden is in general a difficult challenge in formal verification. In this review, we begin by briefly presenting background on reachability analysis with an emphasis on the HJ formulation. We then present recent work showing how high-dimensional reachability verification can be made more tractable by focusing on two areas of development: system decomposition for general nonlinear systems, and traffic protocols for unmanned airspace management. By tackling the curse of dimensionality, tractable verification of practical systems is becoming a reality, paving the way for more pervasive and safer automation.

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2

De Smet, Olivier, Jean-Jacques Lesage, and Jean-Marc Roussel. "Formal Verification of Industrial Control Systems." IFAC Proceedings Volumes 34, no. 17 (2001): 183–88. http://dx.doi.org/10.1016/s1474-6670(17)33277-9.

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3

Zhang, Chi, Wenjie Ruan, and Peipei Xu. "Reachability Analysis of Neural Network Control Systems." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 15287–95. http://dx.doi.org/10.1609/aaai.v37i12.26783.

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Neural network controllers (NNCs) have shown great promise in autonomous and cyber-physical systems. Despite the various verification approaches for neural networks, the safety analysis of NNCs remains an open problem. Existing verification approaches for neural network control systems (NNCSs) either can only work on a limited type of activation functions, or result in non-trivial over-approximation errors with time evolving. This paper proposes a verification framework for NNCS based on Lipschitzian optimisation, called DeepNNC. We first prove the Lipschitz continuity of closed-loop NNCSs by unrolling and eliminating the loops. We then reveal the working principles of applying Lipschitzian optimisation on NNCS verification and illustrate it by verifying an adaptive cruise control model. Compared to state-of-the-art verification approaches, DeepNNC shows superior performance in terms of efficiency and accuracy over a wide range of NNCs. We also provide a case study to demonstrate the capability of DeepNNC to handle a real-world, practical, and complex system. Our tool DeepNNC is available at https://github.com/TrustAI/DeepNNC.

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4

Hoxha, Bardh. "Verification and Control for Autonomous Mobile Systems." Electronic Proceedings in Theoretical Computer Science 361 (July 10, 2022): 7–8. http://dx.doi.org/10.4204/eptcs.361.3.

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5

HASEGAWA, Masami. "S172026 SIL Verification of Safety Control Systems." Proceedings of Mechanical Engineering Congress, Japan 2013 (2013): _S172026–1—_S172026–4. http://dx.doi.org/10.1299/jsmemecj.2013._s172026-1.

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6

Feketa, Petro, Sergiy Bogomolov, and Thomas Meurer. "Safety Verification for Impulsive Systems." IFAC-PapersOnLine 53, no. 2 (2020): 1949–54. http://dx.doi.org/10.1016/j.ifacol.2020.12.2589.

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7

Rasina, Irina Viktorovna, and Oles Vla\-di\-mi\-ro\-vich Fesko. "Sufficient relative minimum conditions for discrete-continuous control systems." Program Systems: Theory and Applications 11, no. 2 (2020): 61–73. http://dx.doi.org/10.25209/2079-3316-2020-11-2-61-73.

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In this paper, we derive sufficient relative minimum conditions for discrete-continuous control systems on the base of Krotov’s sufficient optimality conditions counterpart. These conditions can be used as verification conditions for suggested control mode and enable one to construct new numerical methods.

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8

Rawlings, Blake C., Jinkyung Kim, Il Moon, and B. Erik Ydstie. "Symbolic Verification of Control Systems and Operating Procedures." Industrial & Engineering Chemistry Research 53, no. 13 (2014): 5299–310. http://dx.doi.org/10.1021/ie402998g.

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9

Mosterman, Pieter J., Gautam Biswas, and Janos Sztipanovits. "Hybrid Modeling and Verification of Embedded Control Systems." IFAC Proceedings Volumes 30, no. 4 (1997): 33–38. http://dx.doi.org/10.1016/s1474-6670(17)43608-1.

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10

Norman, Gethin, David Parker, and Xueyi Zou. "Verification and control of partially observable probabilistic systems." Real-Time Systems 53, no. 3 (2017): 354–402. http://dx.doi.org/10.1007/s11241-017-9269-4.

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