TY - GEN
T1 - Data-Driven Model Invalidation for Unknown Lipschitz Continuous Systems via Abstraction
AU - Jin, Zeyuan
AU - Khajenejad, Mohammad
AU - Yong, Sze Zheng
N1 - Funding Information:
★ These authors contributed equally to this paper. The authors are with the School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, USA (email: {zjin43,mkhajene,szyong}@asu.edu) This work was supported in part by DARPA grant D18AP00073.
PY - 2020/7
Y1 - 2020/7
N2 - In this paper, we consider the data-driven model invalidation problem for Lipschitz continuous systems, where instead of given mathematical models, only prior noisy sampled data of the systems are available. We show that this data-driven model invalidation problem can be solved using a tractable feasibility check. Our proposed approach consists of two main components: (i) a data-driven abstraction part that uses the noisy sampled data to over-approximate the unknown Lipschitz continuous dynamics with upper and lower functions, and (ii) an optimization-based model invalidation component that determines the incompatibility of the data-driven abstraction with a newly observed length-T output trajectory. Finally, we discuss several methods to reduce the computational complexity of the algorithm and demonstrate their effectiveness with a simulation example of swarm intent identification.
AB - In this paper, we consider the data-driven model invalidation problem for Lipschitz continuous systems, where instead of given mathematical models, only prior noisy sampled data of the systems are available. We show that this data-driven model invalidation problem can be solved using a tractable feasibility check. Our proposed approach consists of two main components: (i) a data-driven abstraction part that uses the noisy sampled data to over-approximate the unknown Lipschitz continuous dynamics with upper and lower functions, and (ii) an optimization-based model invalidation component that determines the incompatibility of the data-driven abstraction with a newly observed length-T output trajectory. Finally, we discuss several methods to reduce the computational complexity of the algorithm and demonstrate their effectiveness with a simulation example of swarm intent identification.
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U2 - 10.23919/ACC45564.2020.9147725
DO - 10.23919/ACC45564.2020.9147725
M3 - Conference contribution
AN - SCOPUS:85089561756
T3 - Proceedings of the American Control Conference
SP - 2975
EP - 2980
BT - 2020 American Control Conference, ACC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 American Control Conference, ACC 2020
Y2 - 1 July 2020 through 3 July 2020
ER -