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강화학습 기반 강인 제어를 위한 과거 입출력 정보 필요성 탐구
심형보(Hyungbo Shim),김정우(Jeong Woo Kim),박주영(Jooyoung Park) 대한전기학회 2021 전기학회논문지 Vol.70 No.12
Reinforcement learning yields a feedback controller that achieves specific control goal (which is often translated as a reward function). However, it often suffers from the Sim2Real gap, and domain randomization is known to be a method to overcome this issue. In this paper, we demonstrate necessity of input/outpu history when domain randomization is employed by a formal example and a simulation result. This is equivalent to the necessity of dynamic feedback controller in terms of control theory.
Nonlinear Observers Robust to Measurement Disturbances in an ISS Sense
Shim, Hyungbo,Liberzon, Daniel IEEE 2016 IEEE transactions on automatic control Vol.61 No.1
<P>This paper formulates and studies the concept of quasi-Disturbance-to-Error Stability (qDES) which characterizes robustness of a nonlinear observer to an output measurement disturbance. In essence, an observer is qDES if its error dynamics are input-to-state stable (ISS) with respect to the disturbance as long as the plant's input and state remain bounded. We develop Lyapunov-based sufficient conditions for checking the qDES property for both full-order and reduced-order observers. We use these conditions to show that several well-known observer designs yield qDES observers, while some others do not. Our results also enable the design of novel qDES observers, as we demonstrate with examples. When combined with a state feedback law robust to state estimation errors in the ISS sense, a qDES observer can be used to achieve output feedback control design with robustness to measurement disturbances. As an application of this idea, we treat a problem of stabilization by quantized output feedback.</P>