• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.522-525
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• 2015

In this paper, we propose a walking aid system that guides route to visually impaired people in order to recognize uncertain obstacles based on tactile stimulation. The proposed system is composed of the touch-based obstacle detection module, the obstacle height detection module, and the route guidance algorithms. The touch-based obstacle detection module detects each obstacle, which is located at left, right, and front of a visually impaired person by stimulating his thumb with the rotational force of the servomotor. The obstacle height detection module integrates detected data by the linear arrangement of ultrasonic sensors to identify the height of an obstacle about 3 of-phase(i.e., high, medium, low). The proposed route guidance algorithm guides an optimized path to the visually impaired person by updating his current position information based on the signal of the built-in GPS receiver in smartphone. In addition, the route guidance algorithm delivers information with speech to a visually impaired person through Bluetooth commuination in the developed route guidance app. The proposed system can create a path to avoid the obstacles by recognizing the placed situation of the obstacles with exploring the uncertain path.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.401-406
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• 2004

In this paper, a new time-domain adaptive predistortion scheme is proposed to compensate for the nonlinearity of high power amplifiers (HPA) in OFDM systems. A complex Wiener-Hammerstein model (WHM) is adopted to describe the input-output relationship of unknown HPA with linear dynamics, and a power series model with memory (PSMWM) is used to approximate the HPA expressed by WHM. By using the PSMWM, the compensation input to HPA is calculated in a real-time manner so that the linearization from the predistorter input to the HPA output can be attained even if the nonlinear input-output relation of HPA is uncertain and changeable. In numerical example, the effectiveness of the proposed method is confirmed and compared with the identification method based on PSMWM.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.189-194
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• 1994

Adaptive control systems based upon the command generator tracker(CGT) approach have attracted considerable interest because of the simple structure of its adaptive controller. Some attempts to such improve the adaptive control algorithm, for the sake of the application to broader class of plants, are made. Recently, Su and Sobel(1992) proposed that those schemes can be treated by an unified theory using a metasystem representation with some types of supplementary dynamics. However, in their method, it is difficult to find the dynamic compensator, which is proper and output feedback stabilizable, for the uncertain plant. This paper proposes a new design method of such supplementary dynamics and some parameters of adaptive control system for linear time invariant SISO plants. The method gives a concrete and systematic design method using only a few priori knowledge of the plant.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1208-1211
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• 2010

We propose a variable structure control design method for a class of multivariable uncertain state-delayed systems which can be represented by polytopic models. In terms of LMIs, we derive a sufficient condition for the existence of a linear sliding surface guaranteeing the asymptotic stability of the sliding mode dynamics. We parameterize the sliding surface by using the solution of the LMI existence condition. We also give a switching feedback control strategy guaranteeing stable sliding mode. By using a numerical example, we show that our method supplements the existing results and it can be better than the existing results.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.129-134
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• 1992

Fuzzy theory has shown good control performance for non-linear system that is difficult to be controlled by the conventional controller. Backpropagation neural network can interpolate output without the priori knowledge of its dynamics. In this paper, we proposes a Fuzzy-Neural Controller. The Fuzzy Control by deterministic rule may not be sensitive for uncertain conditions and has a disadvantage of setting the rule by repeatedly experience. To solve such problems, we construct Self organizing Fuzzy-Neural Controller which can reorganize the fuzzy rule according to the state of system. Experimental results show that proposed Fuzzy-Neural Controller has better performance than conventional controller(PID) has especially rising time and overshoot characteristics.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.490-495
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• 1998

In this paper an asymmetric hydraulic actuator which consists of single acting cylinder and servo valve is modeled for a quarter car active suspension system. This model regards the force as an internal state rather than a control input. The control input of the model is the sum of oil flows that pass through the valve's orifices. The resulting dynamic equation in the state space ap-pears a feedback connection of a nominal linear time in-variant term with a nonlinear bounded uncertain block. Since this model makes it possible to eliminate the force control phase, analysis and controller design are made straightforward and simple. Well known LQR method is then applied. Simulation and test rig experiment show the effectiveness of this approach in modeling and control.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.496-508
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• 1999

Fuzzy System which are based on membership functions and rules can control nonlinear uncertain complex systems well. However Fuzzy logic controller(FLC) has problems; It is difficult to design the stable FLC and FLC depends mainly on individual experience. Although FLC can be designed using the error back-propagation algorithm it takes long time to converge into global optimal parameters. Well-developed linear system theory should not be replaced by FLC but instead it should be suitably used with FLC. A new methodology is introduced for designing THEN-PART membership functions of FLC based on its well-tuned state feedback controller. A example of inverted pendulum is given for demonstration of the robustness of proposed methodology.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1705-1714
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• 2018

This paper develops a novel controller called iterative learning sliding mode (ILSM) to control linear and nonlinear fractional-order systems. This control applies a combination structures of continuous and discontinuous controller, conducts the system output to the desired output and achieve better control performance. This controller is designed in the way to be robust against the external disturbance. It also estimates unknown parameters of fractional-order systems. The proposed controller unlike the conventional iterative learning control for fractional systems does not need to apply direct control input to output of the system. It is shown that the controller perform well in partial and complete observable conditions. Simulation results demonstrate very good performance of the iterative learning sliding mode controller for achieving the desired control objective by increasing the number of iterations in the control loop.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.21 no.3
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• pp.1-8
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• 2013

An application of adaptive flight controller is required for the non-linear and high uncertain system that configuration of tiltrotor aircraft is dramatically changed from rotary wing mode to fixed wing mode. In this paper, the applicable adaptive controller for the tiltrotor aircraft was designed using Neural Networks and DMI (Dynamic Model Inversion). The performance of the SCAS (Stability and Control Augmentation System) was simulated against manned military specification, using the fullscale model of 'Smart UAV(Unmanned Aerial Vehicle)' developed by Korea Aerospace Research Institute. And Neural Networks based adaptive controller was verified through its whole operating envelope using the established HQ (Handling Quality) criteria.

• Journal of Mechanical Science and Technology
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• v.16 no.11
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• pp.1379-1394
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• 2002

In case of a single input single output (SISO) system with a nonlinear term, a signal compression method is useful to identify a system because the equivalent impulse response of linear part from the system can be extracted by the method. However even though the signal compression method is useful to estimate uncertain parameters of the system, the method cannot be directly applied to a unique system with hysteresis characteristics because it cannot estimate all of the two different dynamic properties according to its motion direction. This paper proposes a signal compression method with a pre-processor to identify a unique system with two different dynamics according to its motion direction. The pre-processor plays a role of separating expansion and retraction properties from the system with hysteresis characteristics. For evaluating performance of the proposed approach, a simulation to estimate the assumed unknown parameters for an arbitrary known model is carried out. A motion platform with several single-rod cylinders is a representative unique system with two different dynamics, because each single-rod cylinder has expansion and retraction dynamic properties according to its motion direction. The nominal constant parameters of the motion platform are experimentally identified by using the proposed method. As its application, the identified parameters are applied to a design of a sliding mode controller for the simulator.