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Lee, Dongpil,Yi, Kyongsu,Chang, Sehyun,Lee, Byungrim,Jang, Bongchoon Elsevier 2018 Mechatronics Vol.49 No.-
<P><B>Abstract</B></P> <P>This paper describes robust steering assist torque control of electric-power-assisted-steering (EPAS) systems for a target steering wheel torque tracking. The steering assist torque control algorithm has been developed to overcome the major disadvantage of the conventional method of time-consuming tuning to achieve the desired steering feel. A reference steering wheel torque map was designed by post-processing data obtained from target performance vehicle tests with a highly-rated steering feel for both sinusoidal and transition steering inputs. Adaptive sliding-mode control was adopted to ensure robustness against uncertainty in the steering system, and the equivalent moment of inertia damping coefficient and effective compliance were adapted to improve tracking performance. Effective compliance played a role in compensating the error between the nominal rack force and the actual rack force. The performance of the proposed controller was evaluated by conducting computer simulations and a hardware-in-the-loop simulation (HILS) under various steering conditions. Optimal steering wheel torque tracking performances were successfully achieved by the proposed EPAS control algorithm.</P>
Post Injection을 이용한 Diesel Engine의 Turbo Lag개선 및 가속성능 향상
윤동필(Dongpil Yoon),박상운(Sangun Park),이근봉(Keunbong Lee),박영현(Younghyun Park),김숭기(Soongkee Kim) 한국자동차공학회 2010 한국자동차공학회 학술대회 및 전시회 Vol.2010 No.11
Growing concern about increasing greenhouse gases (GHG) today, automobile industry is highly requiring stricter emission regulations and improved fuel consumption. It means that engine development should be more focused on a high efficiency and small displacement engine. To achieve for this demand, one of the effective method is reduction of the engine size by using a turbocharger or supercharger. Turbocharged boost technologies are able to increase thermal efficiency. However, the turbocharged downsized engines generally have worse response than the naturally aspirated (NA) engines because it takes a few seconds to get the turbocharger rotate up to high speed, usually called "Turbo-lag". In order to solve this matter, one possible way is hard solution: some changes in intake/exhaust layout and turbo inertia can be considered, or more sophisticated systems such as a two-stage turbocharger or a supercharger. Different way is soft solution to avoid it: some variables to control the turbocharging systems are optimized. This paper deals with the transient response of a common rail diesel engine with variable geometry turbocharger (VGT). In the paper, a DFSS method was used and also analyzed each factor which influence to turbo lag. The researches discussed focused on soft solution from the result of this analysis. The control strategy is based on the optimization of post injection with conventional VGT control at various transient engine conditions. Also, we developed the algorithm of driver’s intention to accelerate rapidly based on the driver demand. Reflecting driver’s intention selectively, the control strategy for transient response is capable of improving the vehicle acceleration performance with minimum deterioration of total smoke emissions and fuel consumption during the sudden acceleration.
문제 상위는 유전 알고리즘의 성능에 어떤 영향을 미치는가?
유동필(DongPil Yu),김용혁(YongHyuk Kim) 인문사회과학기술융합학회 2018 예술인문사회융합멀티미디어논문지 Vol.8 No.4
수학과 컴퓨터 과학 분야에서 최적화 문제란 가능한 모든 해 중에서 가장 좋은 해를 찾는 문제이다. 유전 알고리즘에서 최적화 문제의 어려운 정도는 상위의 측면에서 설명될 수 있다. 생물학에서 상위는 유전자의 표현형이 하나 혹은 그 이상의 유전자에 의해 억제되는 것을 의미하지만 진화 알고리즘에서는 유전자들 사이의 상호작용을 의미한다. 본 논문에서는 상위와 유전 알고리즘이 최적 해를 찾는 시간 사이의 상관관계를 실험적으로 확인하였다. Shannon의 정보 이론에 근거해 상위를 수치화하는 프레임워크를 사용하여 다양한 문제(One-Max, Royal Road, NK-Landscape)의 상위를 비교하였고, 그 결과 상위가 커짐에 따라 문제가 어려워져 최적 해를 찾기 어려운 경향이 있음을 확인하였다. 성능은 주어진 세대 안에 최적 해를 찾는 경우 최적 해를 찾는 데까지 걸린 세대 수로 비교하였고, 최적 해를 찾지 못하는 경우 최적 해의 적합도에 대한 주어진 세대 동안 찾은 적합도가 가장 높은 해의 적합도 비율로 비교하였다. In mathematics and computer science, an optimization problem is the problem of finding the best solution from feasible ones. In the context of genetic algorithm, the difficulty of an optimization problem can be explained in terms of problem epistasis. In biology, epistasis means that the phenotype of a gene is suppressed by one or more genes, but in an evolutionary algorithm it means the interaction between genes. In this paper, we experimentally show that problem epistasis and the performance of genetic algorithm are closely related. We compared problem epistasis (One-Max, Royal Road, and NK-Landscape) using a framework that quantifies problem epistasis based on Shannon’s information theory, and could show that problem becomes more difficult as problem epistasis grows. In the case that a genetic algorithm finds the optimal solution, performance is compared through the number of generations, otherwise through the ratio of the fitness of the optimal solution to that of the best solution.