List of English Publication

@article{VariationalEncryptedModelPredictiveControl,

title = {Variational Encrypted Model Predictive Control},

author = {Jihoon Suh and Yeongjun Jang and Junsoo Kim and Takashi Tanaka},

doi = {10.1109/LCSYS.2026.3699391},

issn = {2475-1456},

year  = {2026},

date = {2026-06-02},

urldate = {2026-06-02},

journal = {IEEE CONTROL SYSTEMS LETTERS},

volume = {10},

pages = {529-534},

abstract = { We develop a variational encrypted model

predictive control (VEMPC) protocol whose online

execution relies only on encrypted polynomial operations.

The proposed approach reformulates the MPC problem

into a sampling-based estimator, in which the computation

of the quadratic cost is naturally handled by tilting the

sampling distribution, thus reducing online encrypted

computation. The resulting protocol requires no additional

communication rounds or intermediate decryption, and

scales efficiently through two complementary levels of

parallelism. We analyze the effect of encryption-induced

errors on optimality, and simulation results demonstrate

the practical applicability of the proposed method.},

keywords = {Controller encryption, Homomorphic encryption, Model predictive control},

pubstate = {published},

tppubtype = {article}

}

@article{Kim21,

title = {Model Predictive Control Method for Autonomous Vehicles Using Time-Varying and Non-Uniformly Spaced Horizon},

author = {Minsung Kim and Donggil Lee and Joonwoo Ahn and Minsoo Kim and Jaeheung Park},

doi = {https://doi.org/10.1109/ACCESS.2021.3088937},

issn = {2169-3536},

year  = {2021},

date = {2021-06-01},

journal = {IEEE Access},

volume = {9},

pages = {86475-86487},

abstract = {This paper proposes an algorithm for path-following and collision avoidance of an autonomous vehicle based on model predictive control (MPC) using time-varying and non-uniformly spaced horizon. The MPC based on non-uniformly spaced horizon approach uses the time intervals that are small for the near future, and time intervals that are large for the distant future, to extend the length of the whole prediction horizon with a fixed number of prediction steps. This MPC has the advantage of being able to detect obstacles in advance because it can see the distant future. However, the presence of longer time interval samples may lead to poor path-following performance, especially for paths with high curvature. The proposed algorithm performs proper adjustment of the prediction interval according to a given situation. For sections with large curvature, it uses the short prediction intervals to increase the path-following performance; further, to consider obstacles over a wider range, it uses the long prediction intervals. This technique allows simultaneous improvement of the path-following performance and the range of obstacle avoidance with fixed computational complexity. The effectiveness of the proposed method is verified through an open-source simulator, CARLA and real-time experiments.},

keywords = {Autonomous vehicle, Collision avoiadance, Model predictive control, Path following},

pubstate = {published},

tppubtype = {article}

}