• Journal of Advanced Marine Engineering and Technology
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• v.33 no.6
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• pp.932-938
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• 2009

As a stringing troy wire is installed by manual operation, it is necessary to scheme the automatic system for stringing troy wire. To accomplish a task of this kind, in this paper an approach to designing controllers for the hybrid Position/Tension control of a stringing troy wire is presented. Position control system is designed based on equation of dc motor and motion equation of robot, it is controlled by feedback with a detected speed dc motor. Tension control system is designed based on equation of ac servomotor for generating torque and dynamic equation of a troy wire, it is controled by feedback with a detected tension. The control parameters is determined by simulation in independence operation of each system. To suppress a mutual interference that the disturbance occur in operating of two task at same time. Dynamic hybrid control is proposed by feed forward compensator with a disturbance accelerator and a step torque at start. The operation of proposed system is simulated and experimented, results is verified the utilities.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.674-680
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• 2007

The probabilistic roadmap (PRM) method, which is a popular path planning scheme, for a manipulator, can find a collision-free path by connecting the start and goal poses through a roadmap constructed by drawing random nodes in the free configuration space. PRM exhibits robust performance for static environments, but its performance is poor for dynamic environments. On the other hand, reinforcement learning, a behavior-based control technique, can deal with uncertainties in the environment. The reinforcement learning agent can establish a policy that maximizes the sum of rewards by selecting the optimal actions in any state through iterative interactions with the environment. In this paper, we propose efficient real-time path planning by combining PRM and reinforcement learning to deal with uncertain dynamic environments and similar environments. A series of experiments demonstrate that the proposed hybrid path planner can generate a collision-free path even for dynamic environments in which objects block the pre-planned global path. It is also shown that the hybrid path planner can adapt to the similar, previously learned environments without significant additional learning.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.412-417
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• 2005

The Internet has been widely applied to the remote control system. The network-based control system, however, has a random time delay and an inherent weak point of the network, when the data ate transmitted. The network delay may result in performance degradation or even system instability in teleoperation. In this paper a prediction model of network delay using TSK (Takagi-Sugeno-Kang) fuzzy model is presented. An adaptive scheme is developed to update the prediction model according to the current network status. The prediction model is applied to the control of an Internet-based mobile robot to show its usefulness. In the computer simulation the TSK Prediction model of network delay is proven superior to the conventional algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.203-206
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• 1997

DC servo motors are widely used in many industrial fields as actuator of robot and driving power motors of electrical vehicle. Usually in the speed control systems, of motors, speed sensors are required and this fact results in the increased price and operating cost and the limited application of the motors. In this paper, a new speed control method for DC servo motor is proposed. In the scheme, the rotational speed is estimated by the measurement values of the armature voltage and current, instead of measurement by sensor. Optimal control theory is applied to design of the controller in construction of real system. This paper also report on the results of experiments to prove the validity of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.7-12
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• 1990

In this paper, we propose a new manipulator control scheme based on the CMAG neural network. The proposed control consists of two components. The feedforward component is an output of trained CMAC neural network and the feedback component is a modified sliding mode control. The CMAC accepts the position, velocity and acceleration of manipulator as input and outputs two values for the controller : One is the nominal torque used for feedforward compensation(M1 network) and the other is the inertia matrix related information used for the feedback component(M2 network). Since the used control algorithm guarantees the robust trajectory tracking in spite of modeling errors, the CMAC mapping errors due to the memory limitation are little worth consideration.

• Journal of Mechanical Science and Technology
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• v.18 no.3
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• pp.337-346
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• 2004

The control scheme using fuzzy modeling and Parallel Distributed Compensation (PDC) concept is proposed to provide asymptotic tracking of a reference signal for the flexible joint manipulators with uncertain parameters. From Lyapunov stability analysis and simulation results, the developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop multi-input/multi-output system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.

• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.150-153
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• 2007

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.507-512
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• 1992

This paper presents a method for the automatic generation of assembly sequences based on the recursive extraction of a preferred part along with the verification of its disassemblability. To verify the disassemblability of the part we inference the geometric precedence constraints using a method of extracting separable directions for the part and determine the disassemblability cost by the separability and stability cost. The proosed method provides a local optimal solution of finding a cost effective assembly plan, and the feasibility of calculating robot motion programs by evaluating separable directions in flexible manufacturing application. A case study is given to illustrate the concepts and procedure of the proposed scheme.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.533-536
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• 1998

The intent of this paper is to describe a neural network structure called dynamic neural processor(DNP), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. Computer simulations are provided to demonstrate the effectiveness of the proposed learning using the DNP.

• Proceedings of the KAIS Fall Conference
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• 2006.11a
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• pp.122-128
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• 2006

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.