List of English Publication

@article{nokey,

title = {Robust disturbance rejection for uncertain linear system with time-varying exo-system: A disturbance observer approach},

author = {Hyuntae Kim and Gyunghoon Park and Hyungbo Shim},

url = {https://www.sciencedirect.com/science/article/pii/S0005109824005934?dgcid=author},

doi = {10.1016/j.automatica.2024.112100},

issn = { 0005-1098},

year  = {2025},

date = {2025-04-01},

urldate = {2025-04-01},

journal = {Automatica},

issue = {174},

pages = {112100},

abstract = {This paper addresses the problem of completely eliminating modeled disturbance that enters linear minimum-phase systems having parametric uncertainty and is generated by a linear time-varying exo-system. Motivated by the internal model principle (IMP), the core is to append a filter in front of the plant, such that (a) the disturbance model is embedded into the filter dynamics and (b) the filter is of minimum phase. Then the cascade connection of the plant and the filter is robustly stabilized by employing a disturbance observer (DOB)-based controller. In addition to simplicity in the design over the existing IMP-based approaches, the proposed controller also enjoys benefits of the DOB techniques, such as robustness against large parametric uncertainty and additional unmodeled disturbance, and guaranteeing a pre-defined nominal tracking performance in both transient and steady-state. Stability and performance of the controller are analyzed by the singular perturbation theory.},

howpublished = {Automatica},

keywords = {Disturbance observer, Internal model principle, Linear Systems},

pubstate = {published},

tppubtype = {article}

}

@article{ParkShimJoo18,

title = {Recovering Nominal Tracking Performance in an Asymptotic Sense for Uncertain Linear Systems},

author = {Gyunghoon Park and Hyungbo Shim and Youngjun Joo},

doi = {10.1137/17M1122657},

year  = {2018},

date = {2018-03-01},

journal = {SIAM Journal on Control and Optimization},

volume = {56},

number = {2},

pages = {700-722},

abstract = {In this paper, we consider the problem of recovering a (predefined) nominal output trajectory in the presence of model uncertainty and external disturbance. In particular, whereas the nominal performance recovery (NPR) has been studied in an approximate fashion in the literature, we extend the notion of the NPR in an asymptotic sense from the perspective of the internal model principle: that is, as long as the disturbance and reference signals are generated by an exogenous system, the actual output not only is kept close to the nominal trajectory as much as desired but also asymptotically converges to the nominal one as time elapses. It is shown via the singular perturbation theory that the asymptotic NPR can be achieved for uncertain minimum-phase systems under arbitrarily large (but bounded) model uncertainty. A disturbance observer (DOB) approach is employed in the controller design, with the internal model embedded into the so-called Q-filter, which is a key component of the DOB. Simulation results for mechanical positioning systems illustrate that the asymptotic NPR can enhance robust performance of control systems.},

keywords = {Disturbance observer, Internal model principle, Nominal performance recovery, Robustness},

pubstate = {published},

tppubtype = {article}

}