넙치 및 조피볼락용 습사료의 보관조건에 따른 안정성 평가

안창범,주용석,정관식,서경란,신태선 여수대학교 1998 論文集 Vol.13 No.2

본 실험은 습사료를 대상으로 보관조건(온도별, 시간별)에 따른 지질의 산화진행 정도를 파악하여 사료의 효율적인 이용성을 구명하고자 산가(acid value, AV), 과산화물가(peroxide avlue, POV) 및 비타민 함량을 분석하였다. 생사료와 분말배합사료를 혼합하여 제조한 습사료는 혼합비가 8:2 사료에서 5:5 사료보다 높은 AV와POV를 나타내었고, 보관기간이 경과함에 따라 보관조건과는 상관없이 8:2 사료에서 빠른 산패를 보였다. 산화진행속도는 4℃ 보관조건하에서는 48시간째, -15℃ 보관조건하에서는 72시간째에서 빠르게 일어났다. 60,000 Lux이상의 직사광선 노출하에서의 AV,POV는 노출시간이 길어질수록 증가하였고, 8:2사료에서 빠르게 변화하였다. This experiment was conducted to investigate rancidity in moist pellet(MP) during various practical condition of handling and storage conditions. The experimental moist pellet diets were prepared by mixing frozen raw fish (FRF) and commercial compound meal (CCM) in ratio of 8:2 and 5:5, respectively. Immediately before and after manufacturing of MP, the MPs were stored at 4℃ and -15℃, and exposed under the sunlight(30±2℃, 60000Lux). The rancidity of each MP was determined from 1 to 96 hours after pellting. The acid value (AV) and peroxide value (POV) in the diets stored at 4 and -15℃ increased rapidly after 48 and 72 hours, respectively. For the sample exposed sunlight, AV and POV were slightly increased with the exposed time. The rancidity increased in the 8:2MP(FRF : CCM) than in the 5:5MP(ERF : CCM) at all storage condition and the amount of vitamin E in MPs decreased rapidly as AV and POV increased.

Model Based Compensator for Rate-Dependent Hysteresis in DEAP Actuator

Ahn Kyoung Kwan,Bui Ngoc Minh Truong 대한기계학회 2013 대한기계학회 춘추학술대회 Vol.2013 No.5

Nonlinear PID Control to Improve the Control Performance of the Pneumatic Artificial Muscle Manipulator Using Neural Network

Kyoung Kwan Ahn,TU Diep Cong Thanh 대한기계학회 2005 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.19 No.1

A novel actuator system which has achieved increased popularity to provide these advantages such as high strength and power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available, cheap power source, inherent safety and mobility assistance to humans performing tasks has been the utilization of the pneumatic artificial muscle (PAM) manipulator, in recent times. However, the complex nonlinear dynamics of the PAM manipulator makes it a challenging and appealing system for modeling and control design. The problems with the time variance, compliance, high hysteresis and nonlinearity of pneumatic systems have made it difficult to realize precise position control with high speed. In order to realize satisfactory control performance, the effect of nonlinear factors contained in the PAM manipulator must be considered. The purpose of this study is to improve the control performance of the PAM manipulator using a nonlinear PID controller. Superb mixture of conventional PID controller and the neural network, which has powerful capability of learning, adaptation and tackling nonlinearity, brings us a novel nonlinear PID controller using neural network. This proposed controller is appropriate for a kind of plants with nonlinearity uncertainties and disturbances. The experiments were carried out in practical PAM manipulator and the effectiveness of the proposed control algorithm was demonstrated through the experiments, which suggests its superior performance and disturbance rejection.

Improvement of the Control Performance of Pneumatic Artificial Muscle Manipulators Using an Intelligent Switching Control Method

Ahn, Kyoung-Kwan,Thanh, TU Diep Cong The Korean Society of Mechanical Engineers 2004 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.18 No.8

Problems with the control, oscillatory motion and compliance of pneumatic systems have prevented their widespread use in advanced robotics. However, their compactness, power/weight ratio, ease of maintenance and inherent safety are factors that could be potentially exploited in sophisticated dexterous manipulator designs. These advantages have led to the development of novel actuators such as the McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle Manipulators. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external inertia load in the pneumatic artificial muscle manipulator. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is newly proposed. This estimates the external inertia load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external inertia loads.

Adaptive Backstepping Control of an Electrohydraulic Actuator

Kyoung Kwan Ahn,Doan Ngoc Chi Nam,Maolin Jin IEEE 2014 IEEE/ASME transactions on mechatronics Vol.19 No.3

<P>This paper presents an adaptive position control for a pump- controlled electrohydraulic actuator (EHA) based on an adaptive backstepping control scheme. The core feature of this paper is the combination of a modified backstepping algorithm with a special adaptation law to compensate all nonlinearities and uncertainties in EHA system. First of all, the mathematical model of the EHA is developed. The position control is then formulated using a modified backstepping technique and the uncertainties in hydraulic system are adapted by employing a special Lyapunov function. The control signal consists of an adaptive control signal to compensate the uncertainties and a simple robust structure to ensure the robustness corresponding to a bounded disturbance. Experimental results proved strongly the ability of the proposed control method.</P>

Intelligent Switching Control of the Pneumatic Artificial Muscle Manipulators

Kyoung Kwan AHN,TU Diep Cong Thanh 제어로봇시스템학회 2004 제어로봇시스템학회 국제학술대회 논문집 Vol.2004 No.8

Inverse Double NARX Fuzzy Modeling for System Identification

Kyoung Kwan Ahn,Ho Pham Huy Anh IEEE 2010 IEEE/ASME transactions on mechatronics Vol.15 No.1

<P>In this paper, a novel inverse double nonlinear autoregressive with exogenous input (NARX) fuzzy model is applied to simultaneously model and identify both joints of the prototype two-axis pneumatic artificial muscle (PAM) robot arm's inverse dynamic model. Highly nonlinear features of both joints of the nonlinear manipulator system are identified by the proposed inverse double NARX fuzzy (IDNF) model based on experimental input-output training data. The modified genetic algorithm (GA) optimally generates the appropriate fuzzy if-then rules to perfectly characterize the dynamic features of the two-axis PAM manipulator system. The evaluation of different IDNF models with various ARX model structures will be discussed. For the first time, the nonlinear IDNF model of the two-axis PAM robot arm is investigated. The results show that the nonlinear IDNF model that is trained by GA performs better and has a higher accuracy than the conventional inverse fuzzy model.</P>

Force Control of Hybrid Actuator Using Learning Vector Quantization Neural Network

Kyoung Kwan AHN,NGUYEN Huynh Thai Chau 대한기계학회 2006 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.20 No.4

Hydraulic actuators are important in modern industry due to high power, fast response, and high stiffness. In recent years, hybrid actuation system, which combines electric and hydraulic technology in a compact unit, can be adapted to a wide variety of force, speed and torque requirements. Moreover, the hybrid actuation system has dealt with the energy consumption and noise problem existed in the conventional hydraulic system. Therefore, hybrid actuator has a wide range of application fields such as plastic injection-molding and metal forming technology, where force or pressure control is the most important technology. In this paper, the solution for force control of hybrid system is presented. However, some limitations still exist such as deterioration of the performance of transient response due to the variable environment stiffness. Therefore, intelligent switching control using Learning Vector Quantization Neural Network (LVQNN) is newly proposed in this paper in order to overcome these limitations. Experiments are carried out to evaluate the effectiveness of the proposed algorithm with large variation of stiffness of external environment. In addition, it is understood that the new system has energy saving effect even though it has almost the same response as that of valve controlled system.

Robust force control of a hybrid actuator using quantitative feedback theory

Kyoung Kwan Ahn,Nguyen Huynh Thai Chau,Dinh Quang Truong 대한기계학회 2007 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.21 No.12

The use of hydraulic systems in industrial applications has become widespread due to their advantages in efficiency. In recent years, hybrid actuation systems, which combine electric and hydraulic technology into a compact unit, have been adapted to a wide variety of force, speed and torque requirements. A hybrid actuation system resolves energy consumption and noise problems characteristic of conventional hydraulic systems. A new, low-cost hybrid actuator using a DC motor is considered to be a novel linear actuator with various applications such as robotics, automation, plastic injection-molding, and metal forming technology. However, this efficiency gain is often accompanied by a degradation of system stability and control problems. In this paper, to satisfy robust performance requirements, tracking performance specifications, and disturbance attenuation requirements, the design of a robust force controller for a new hybrid actuator using Quantitative Feedback Theory (QFT) is presented. A family of plant models is obtained from measuring frequency responses of the system in the presence of significant uncertainty. Experimental results show that the hybrid actuator can achieve highly robust force tracking even when environmental stiffness set-point force varies. In addition, it is understood that the new system reduces energy use, even though its response is similar to that of a valve-controlled system.

Position Control of Shape Memory Alloy Actuators Using Self Tuning Fuzzy PID Controller

Kyoung Kwan Ahn,Bao Kha Nguyen 제어로봇시스템학회 2006 International Journal of Control, Automation, and Vol.4 No.6