TY - GEN
T1 - Active Model Discrimination Using Partition-Based Output Feedback Input Design
AU - Shen, Qiang
AU - Yong, Sze Zheng
N1 - Funding Information:
1School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, P.R. China (e-mail: qiang.shen@sjtu.edu.cn) 2School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, USA (e-mail: szyong@asu.edu. This work was done at Arizona State University and was supported in part by DARPA grant D18AP00073.
Publisher Copyright:
© 2020 EUCA.
PY - 2020/5
Y1 - 2020/5
N2 - In this paper, we propose a partition-based output feedback active model discrimination approach that generates optimal output feedback inputs in a fixed time horizon for separating a set of discrete-time affine models subject to uncontrolled inputs, noises and uncertain initial conditions. Instead of computing the optimal input by solving a parametric mixed-integer linear program (MILP) at run time, we move this computationally demanding optimization task offline by partitioning the measurement domain and building a partition tree over the fixed time horizon. Since output measurements are available at each time instant during run time, we can update the separating input correspondingly and improve the model discrimination performance by reducing the input cost. The effectiveness of the proposed approach is demonstrated through simulations for identifying intention models of human-driven vehicles in a lane changing scenario.
AB - In this paper, we propose a partition-based output feedback active model discrimination approach that generates optimal output feedback inputs in a fixed time horizon for separating a set of discrete-time affine models subject to uncontrolled inputs, noises and uncertain initial conditions. Instead of computing the optimal input by solving a parametric mixed-integer linear program (MILP) at run time, we move this computationally demanding optimization task offline by partitioning the measurement domain and building a partition tree over the fixed time horizon. Since output measurements are available at each time instant during run time, we can update the separating input correspondingly and improve the model discrimination performance by reducing the input cost. The effectiveness of the proposed approach is demonstrated through simulations for identifying intention models of human-driven vehicles in a lane changing scenario.
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M3 - Conference contribution
AN - SCOPUS:85090139342
T3 - European Control Conference 2020, ECC 2020
SP - 712
EP - 717
BT - European Control Conference 2020, ECC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th European Control Conference, ECC 2020
Y2 - 12 May 2020 through 15 May 2020
ER -