• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1811-1812
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• 2007

본 논문에서는 Fuzzy-PID Control을 이용한 2축 도립진자를 제어한다. X-Y 축을 움직이는 카트에는 도립진자가 세워져 있으며 카트가 2축의 평면상에 원하는 위치로 빠르고 정확하게 이동할 수 있게 하는 동시에 도립진자의 균형을 깨뜨리지 않고 움직이는 것을 제어의 목표로 한다. Fuzzy-PID Control은 도립진자의 균형에 대한 제어뿐만 아니라 로봇의 위치 제어까지 적용된다. 본 연구 논문에서는 이러한 Fuzzy-PID 적용 시스템에 대한 시뮬레이션을 실행함으로 그 우수성을 입증 하고자 한다.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2081-2083
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• 2001

In this paper, without mathematical modeling dynamics, the plant parameter in sliding mode are estimated by the indirect adaptive fuzzy control. Adaptive laws for fuzzy parameters and fuzzy rule structure are established so that the whole system is stable in the sense of Lyapunov stability. The computer simulation results for inverted pendulum system show the performance of the proposed fuzzy sliding mode controller.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.968-970
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• 2003

Fuzzy Controller is a system that displays a person's thoughts using membership function and IF-THEN rules. With the help of specialists' knowledge, rule bases can be explained in easy language. Furthermore Fuzzy Controller has strong resistance against turbulence. Its performance is especially prominent when targets cannot be measured in mathematic methods because the fuzzy controller can measure the output using only the relations between the input and output. But Fuzzy System has a problem that is calculation speed. I suggest you a theory to solve it. I applied a theory to inverted pendulum. Because it is represent of nonlinear system.

• Journal of the Korea Computer Industry Society
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• v.2 no.7
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• pp.967-974
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• 2001

This paper aims at deriving an equivalent finite dimensional discrete-time system for $H{\infty}$ type problem for sampled-data control systems. A widely used approach is based on the lifting technique, but it needs somewhat complicate computation. Instead, this paper derives an equivalent finite-dimensional discrete-time system directly from a description of the sampled-data system which is achieved via a closed-loop expression of the worst-case intersample disturbance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.356-361
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• 2006

This paper deals with a fault detection system design for nonlinear systems with uncertain time varying parameters modelled as a T-S fuzzy system. A coprime factorization for T-S fuzzy systems is defined and a residual generator is designed using a left coprime factor. A fault detection criteria derived from the residual generator is also suggested. In order to demonstrate the efficacy of the suggested method, the fault detection method is applied to an inverted pendulum system and computer simulations are performed.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.66.4-66
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• 2001

In this paper we have fabricated the two-dimensional Rod Balancing System which expands conventional one-dimensional inverted pendulum control system and designed its controller. The X-axis cart and Y-axis bar of the Rod Balancing System, which is composed of X-Y table, are actuated through timing belt by each of two geared DC motors, and the rod mounted on a X-axis cart can be brought to the desired position and maintained in a vertical position by motor-control. For the control of the Rod Balancing System, we used a fuzzy logic controller that is an approach to systems control when the exact mathematical model of the plant is unknown or the mathematical model is too complex to understand.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.142-145
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• 2003

Many control applications using Neural Network need a priori information about the objective system. But it is impossible to get exact information about the objective system in real world. To solve this problem, several control methods were proposed. Reinforcement learning control using neural network is one of them. Basically reinforcement learning control doesn't need a priori information of objective system. This method uses reinforcement signal from interaction of objective system and environment and observable states of objective system as input data. But many methods take too much time to apply to real-world. So we focus on faster learning to apply reinforcement learning control to real-world. Two data types are used for reinforcement learning. One is reinforcement signal data. It has only two fixed scalar values that are assigned for each success and fail state. The other is observable state data. There are infinitive states in real-world system. So the number of observable state data is also infinitive. This requires too much learning time for applying to real-world. So we try to reduce the number of observable states by classification of states with Self-Organizing Map. We also use neural dynamic programming for controller design. An inverted pendulum on the cart system is simulated. Failure signal is used for reinforcement signal. The failure signal occurs when the pendulum angle or cart position deviate from the defined control range. The control objective is to maintain the balanced pole and centered cart. And four states that is, position and velocity of cart, angle and angular velocity of pole are used for state signal. Learning controller is composed of serial connection of Self-Organizing Map and two Multi-layer Feed-Forward Neural Networks.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.688-693
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• 2013

This paper proposes a new method on attitude control of an underwater robot by using five ABTs (Attitude Ballast Tank). A pipe is connected to the bottom of the ABTs and transfers water by a pump, while another pipe is connected to the top of the ABT to transfer air. The buoyancy center of the underwater robot can be changed by means of the water transfer. This way, the attitude of the underwater robot can be maintained and/or controlled as desired. The changes of the center of gravity and the buoyancy central are estimated by a Lagrangian function which is similar to that for an inverted pendulum. The controller in this paper is designed by modeling of the underwater robot and selecting suitable gains of a PD controller which has fast response characteristics. This paper shows the possibility of the attitude control of an underwater robot by changing the center of gravity and the buoyancy center of the robot. Moreover, experimental results verify that the controller is effective in maintaining Roll/Pitch of the underwater robot with very low power consumption.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.336-341
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• 2015

This paper proposes a balancing and driving control system for a Mecanum wheel ball robot which has a two axis structure and four motors. The inverted pendulum control method is adopted to maintain the balance of the ball robot while it is driving. For the balancing control, an anon-model-based controller has been designed to control the device simply without the need of a complex formula. All the gains of the controller are heuristically adjusted during the experiments. The tilt angle is measured by IMU sensors, which is used to generate the control input of the roll and pitch controller to make the tilt angle zero. For the driving control, the PID control algorithm has been adopted with angles of the wheels and the encoder data. The performance of the designed control system has been verified through the real experiments with the suggested ball robot.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.1133-1138
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• 2012

In the paper, design of nonlinear sliding surfaces which are based on optimal control is studied, The state trajectory by the input of optimal control was obtained by Frobenius theorem and matrix decomposition method, was set the nonlinear sliding surfaces of the system. The states is maintained to sliding surfaces from initial states. As the result, robustness of the system can be guaranteed throughout an entire response of the system starting form the initial time instance, the uncertainty and external disturbance that can occur during the reaching time is removed, the problem of large control input was solved, and setting the sliding surfaces optimal path was able to reduce the tracking time. The validity of the proposed control scheme is shown in computer simulation for inverted pendulum.