• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.194-203
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• 2008

Iterative learning control (ILC) is a simple and effective method for the control of systems that perform the same task repetitively. ILC algorithm uses the repetitiveness of the task to track the desired trajectory. In this paper, we propose a PID (proportional plus integral and derivative) type ILC update law for control discrete-time single input single-output (SISO) linear time-invariant (LTI) systems, performing repetitive tasks. In this approach, the input of controlled system in current cycle is modified by applying the PID strategy on the error achieved between the system output and the desired trajectory in a last previous iteration. The convergence of the presented scheme is analyzed and its convergence condition is obtained in terms of the PID coefficients. An optimal design method is proposed to determine the PID coefficients. It is also shown that under some given conditions, this optimal iterative learning controller can guarantee the monotonic convergence. An illustrative example is given to demonstrate the effectiveness of the proposed technique.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.413-418
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• 1991

Two methods are applied to the problem of trajectory optimization for launch vehicles which burn solid propellant. One is 'Optimal Control' theory, the other is 'NonLinear Programming' method. Trajectory optimization for solid rocket motors has a special problem. The special problem is that the payload of launch vehicle is not the function of control variable. This paper deals with this special problem.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.47-52
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• 1998

This study deals with guidance and control laws for an optimal reentry trajectory of a vertical landing reusable launch vehicle (RLV) in the future. First, a guidance law is designed to create the reference trajectory which minimizes propellant consumption. Then, a nonlinear feedback controller based on a linear quadratic regulator is designed to make the vehicle follow the predetermined reference trajectory, The proposed method is simulated for the first stage of the H-II scale rocket.

• 한국항공우주학회지
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• 제43권12호
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• pp.1062-1070
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• 2015

본 연구에서는 최적 스무딩 필터를 이용한 이동 목표물 궤적 추정을 수행한다. 이동 목표물의 위치와 속도 데이터 확보를 위해 마이크로폰 어레이를 이용한 빔형성 기법이 적용하며, 획득 데이터를 이용한 궤적 추정 성능 향상을 위해 칼만 필터와 최적 스무딩 필터를 설계한다. 목표물의 기동을 고려한 싱어 표적 모델을 필터에 활용한다. 최적 스무딩 필터 검증을 위해 초기 기동을 하는 미사일 시뮬레이션 환경에서 추정 성능을 확인하였으며, 모형 로켓을 이용한 실험을 통해 빔형성 기법과 칼만 필터, 그리고 최적 스무딩 필터의 궤적 추정 성능을 검증하였다. 검증 결과 적용한 필터를 통해 빔형성 기법을 이용한 궤적 추정 성능의 향상을 확인하였으며, 칼만 필터와 비교해 최적 스무딩 필터의 이동 목표물 궤적 추정 정밀도가 향상됨을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1960-1961
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• 2006

In this paper, The trajectory control of robot manipulator is proposed. It divides by trajectory planning and tracking control. A trajectory planning and tracking control of robot manipulator is used to the neural network and evolutionary algorithm. The trajectory planning provides not only the optimal trajectory for a given cost function through evolutionary algorithm but also the configurations of the robot manipulator along the trajectory by considering the robot dynamics. The computed torque method (C.T.M) using the model of the robot manipulators is an effective means for trajectory tracking control. However, the tracking performance of this method is severely affected by the uncertainties of robot manipulators. The Radial Basis Function Networks(RBFN) is used not to learn the inverse dynamic model but to compensate the uncertainties of robot manipulator. The computer simulations show the effectiveness of the proposed method.

• 한국정밀공학회지
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• 제27권1호
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• pp.76-83
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• 2010

There have been many studies regarding development of autonomous excavation system which is helpful in construction sites where repetitive jobs are necessary. Unfortunately, bucket trajectory planning was excluded from the previous studies. Since, the best use of excavator is to dig efficiently; purpose of this research was set to determine an optimized bucket trajectory in order to get best digging performance. Among infinite ways of digging any given path, criterion for either optimal or efficient bucket moves is required to be established. One method is to adopt work know-how from experienced excavator operator; However the work pattern varies from every worker to worker and it is hard to be analyzed. Thus, other than the work pattern taken from experienced operator, we developed an efficiency model to solve this problem. This paper presents a method to derive a bucket trajectory from optimization theory with empirical CLUB soil model. Path is greatly influenced by physical constraints such as geometry, excavator dimension and excavator workspace. By minimizing a energy function under these constraints, an optimal bucket trajectory could be obtained.

• 한국정밀공학회지
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• 제20권8호
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• pp.135-143
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• 2003

This paper presents the impact minimization of a biped robot by using genetic algorithm. In case we want to accomplish the designed plan under the special environments, a robot will be required to have walking capability and patterns with legs, which are in a similar manner as the gaits of insects, dogs and human beings. In order to walk more effectively, studies of mobile robot movement are needed. To generate optimal motion for a biped robot, we employ genetic algorithm. Genetic algorithm is searching for technology that can look for solution from the whole district, and it is possible to search optimal solution from a fitness function that needs not to solve differential equation. In this paper, we generate trajectories of gait and trunk motion by using genetic algorithm. Using genetic algorithm not only on gait trajectory but also on trunk motion trajectory, we can obtain the smoothly stable motion of robot that has the least impact during the walk. All of the suggested motions of biped robot are investigated by simulations and verified through the real implementation.

• ETRI Journal
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• 제39권3호
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• pp.353-363
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• 2017

Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster-head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well-known algorithms.

• 한국정밀공학회지
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• 제30권9호
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• pp.961-968
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• 2013

In this paper, we propose an optimal design for starfish capturing manipulator module with four-bar linkage mechanism. A tool link with compliance is attached on the four-bar linkage, and the tool repeats detaching starfish from the ground and putting it into the storage box. Since the tool is not rigid and the manipulator is operating underwater, the trajectory of the tool tip is determined by its dynamics as well as kinematics. We analyzed the trajectory of the manipulator tool tip by quasi-static analysis considering both kinematics and dynamics. In optimization, the lengths of each link and the tool stiffness are considered as control variables. To maximize the capturing ability, capturing stroke of the four-bar manipulator trajectory is maximized. Reaction force and reaction moment, and other kinematic constraints were considered as inequality constraints.

• Journal of Advanced Marine Engineering and Technology
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• 제38권10호
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• pp.1303-1309
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• 2014

In this paper, we present an iterative learning control (ILC) framework for tracking problems with predefined data points that are desired points at certain time instants. To design ILC systems for such problems, a new ILC scheme is proposed to produce output curves that pass close to the desired points. Unlike traditional ILC approaches, an algorithm will be developed in which the control signals are generated by solving an optimal ILC problem with respect to the desired sampling points. In another word, it is a direct approach for the multiple points tracking ILC control problem where we do not need to divide the tracking problem into two steps separately as trajectory planning and ILC controller.The strength of the proposed formulation is the methodology to obtain a control signal through learning law only considering the given data points and dynamic system, instead of following the direction of tracking a prior identified trajectory. The key advantage of the proposed approach is to significantly reduce the computational cost. Finally, simulation results will be introduced to confirm the effectiveness of proposed scheme.