• Journal of Mechanical Science and Technology
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• v.18 no.4
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• pp.535-549
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• 2004

This paper proposes a sliding mode controller with fuzzy adaptive perturbation compensator(FAPC) to get a good control performance and reduce the chatter, The proposed algorithm can reduce the chattering because the proposed fuzzy adaptive perturbation compensator compensates the perturbation terms. The compensator computes the control input for compensating unmodeled dynamic terms and disturbance by using the observer-based fuzzy adaptive network(FAN) The weighting parameters of the compensate. are updated by on-line adaptive scheme in order to minimize the estimation error and the estimation velocity error of each actuator. Therefore, the combination of sliding mode control and fuzzy adaptive network gives the robust and intelligent routine to get a good control performance. To evaluate the control performance of the proposed approach, tracking control is experimentally carried out for the hydraulic motion platform which consists of a 6-DOF parallel manipulator.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2714-2716
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• 2005

In this paper, we designed the adaptive fuzzy logic controller for crane system using neural network and real-coding genetic algorithm. The proposed algorithm show a good performance on convergence velocity and diversity of population among evolutionary computations. The weights of neural network is adaptively changed to tune the input/output gain of fuzzy logic controller. And the genetic algorithm was used to leam the feedforward neural network. As a result of computer simulation, the proposed adaptive fuzzy logic controller is superior to conventional controllers in moving and modifying the destination point.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.157-160
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• 2001

This paper presents A Design of WMR Controller that being composed of cooperative relation between PID controller and optimized neural network algorithm, it operate a variable control by velocity. Some proposed algorithm in the past just depended on PID controller for the control of position of WMR but for more efficient control we design a variable controller that operate control by PD controller using neural network if it is satisfied with any given condition. it adjust gain of PD controller for real time control using a fast feedforward algorithm which is different with Form of the standard backpropagation algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.180.5-180
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• 2001

This paper presents A Design of WMR Controller that being composed of cooperative relation between PID controller and optimized neural network algorithm, it operate a variable control by velocity. Some proposed algorithm in the past just depended on PID controller for the control of position of WMR but for more efficient control we design a variable controller that operate control by PD controller using neural network if it is satisfied with any given condition. it adjust gain of PD controller for real time control using a fast feedforward algorithm which is different with Form of the standard backpropagation algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.65.2-65
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• 2002

This paper is concerned with the accurate position control of a rodless pneumatic cylinder using On/Off solenoid valve. A novel Intelligent Modified Pulse Width Modulation(MPWM) is newly proposed. The control performance of this pneumatic cylinder depends on the external loads. To overcome this problem , switching of control parameter using artificial neural network is newly proposed, which estimates external loads on rodless pneumatic cylinder using this training neural network. As an underlying controller, a state feedback controller using position, velocity and acceleration is applied in the switching control the system. The effectiveness of the proposed control algorithms are demonstrated...on/off solenoid valve, load estimation, MPWM, Artificial neural network.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.4
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• pp.341-349
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• 2004

In this paper, the decentralized neural network control of the reference compensation technique is proposed to control a 2-DOF inverted pendulum on an x-y plane. The cart with the 2-DOF inverted pendulum moves on the x-y plane and the 2-DOF inverted pendulum rotates freely on the x-y axis. Since the 2-DOF inverted pendulum is divided into two 1-DOF inverted pendulums, the decentralized neural network control is applied not only to balance the angle of pendulum, but also to control the position tracking of the cart. Especially, a circular trajectory tracking is tested for position tracking control of the cart while maintaining the angle of the pendulum. Experimental results show that position control of the inverted pendulum system is successful.

• Journal of Mechanical Science and Technology
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• v.19 no.2
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• pp.529-539
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• 2005

The development of a fast, accurate, and inexpensive position-controlled pneumatic actuator that may be applied to various practical positioning applications with various external loads is described in this paper. A novel modified pulse-width modulation (MPWM) valve pulsing algorithm allows on/off solenoid valves to be used in place of costly servo valves. A comparison between the system response of the standard PWM technique and that of the modified PWM technique shows that the performance of the proposed technique was significantly increased. A state-feedback controller with position, velocity and acceleration feedback was successfully implemented as a continuous controller. A switching algorithm for control parameters using a learning vector quantization neural network (LVQNN) has newly proposed, which classifies the external load of the pneumatic actuator. The effectiveness of this proposed control algorithm with smooth switching control has been demonstrated through experiments with various external loads.

• Journal of Ocean Engineering and Technology
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• v.11 no.1
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• pp.84-95
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• 1997

This paper presents a new approach to the design speed and azimuth control of a mobile robot with drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the frmework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simple the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.30-35
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• 1990

Learning control of a robot manipulator is proposed using the backpropagation neural network. The learning controller is composed of both a linear feedback controller and a neural network-based feedforward controller. The stability analysis of the learning controller is presented. Three energy functions are selected in teaching the neural network controller : 1/2.SIGMA.vertical bar torque error vertical bar $^{2}$, 1/2.SIGMA..alpha. vertical bar position error vertical bar $^{2}$ + .betha. vertical bar velocity error vertical bar $^{2}$ + .gamma. vertical bar acceleration error vertical bar $^{2}$ and learning methods are presented. Simulation results show that the learning controller which is learned to minimize the third energy function performs better than the others in tracking problems. Some properties of the learning controller are discussed with simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.474-474
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• 2000

This paper shows that a system with two-link arm can obtain hand reaching movement to a target object projected on a visual sensor by reinforcement learning using a layered neural network. The reinforcement signal, which is an only signal from the environment, is given to the system only when the hand reaches the target object. The neural network computes two joint torques from visual sensory signals, joint angles, and joint angular velocities considering the urn dynamics. It is known that the trajectory of the voluntary movement o( human hand reaching is almost straight, and the hand velocity changes like bell-shape. Although there are some exceptions, the properties of the trajectories obtained by the reinforcement learning are somewhat similar to the experimental result of the human hand reaching movement.