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Improved Robustness of Reinforcement Learning Based on Uncertainty and Disturbance Estimator
Jinsuk Choi,Hyunbeen Park,Jongchan Baek,Soohee Han 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
This paper proposes a method to improve the robustness of RLs based on model-free uncertainty and disturbance estimator (RL-based UDE). In the real environment, instead of using optimal trajectory and control techniques to perform complex tasks, it learns through RL and supplements robustness by using uncertainty and disturbance estimator (UDE). From UDE, the robotics system can be improved the stability by appropriately canceling the uncertainty and disturbance without efforts to obtain model information; hence the UDE can compensate for the performance degradation of RL when system is non-stationary. In addition, the performance can be improved by reducing the sensor noise from low-pass filter of UDE. It is shown through an experiment that the proposed RL-based UDE provides robustness.
고압 가솔린/디젤, 액상 LPG 분사의 저온 연료분무 특성
최재준(Jaejoon Choi),이석환(Seokhwan Lee),최동석(Dongseok Choi),공장식(Jangsik Kong),강진석(Jinsuk Kang),배충식(Choongsik Bae) 한국자동차공학회 2002 한국자동차공학회 춘 추계 학술대회 논문집 Vol.2002 No.5_1
Engine emission during cold start below zero temperature condition is one of the issues as the emission emitted during that period covers 80% of the total emission for passenger cars. Spray characteristics(vaporization, atomization and penetration) in the low temperature condition is, therefore, important as they affect engine combustion followed by engine-out emission.<br/> A wide range of injectors were tested and characterized. Spray characteristics were evaluated in two typical temperatures( 15°C and -10°C). Spray characteristics with the DISI and HSDI injectors seemed to be independent on temperatures selected in the reported work while LPG injector exhibited the growth of the liquid droplet size and the increase of liquid penetration to lead a difference of engine combustion condition and engine-out emission.