• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2037-2039
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• 2002

This paper focuses on robust $H_{\infty}$ control for bilinear systems with time-varying parameter uncertainties via state feedback. The suitable robustly stabilizing feedback control law can be constructed in term of solution to a state variable x-dependent quadratic Riccati equation using successive approximation technique. Also, the state feedback control law robustly stabilizes the plant and guarantees a robust $H_{\infty}$ performance for the closed-loop bilinear system with parameter uncertainties and exogenous disturbance.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.109-111
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• 2001

Modern power grids are becoming more and more stressed with the load demands increasing continually. Therefore large stressed power systems exhibit complicated dynamic behavior when subjected to small disturbance. Especially, it is needed to analyze special conditions which make small signal stability structure varied according to operating conditions. This paper shows that the relation between small signal stability and operating conditions can be predicted well using node-focus point and 1:1 resonance point. Also, the weak point which limits operating range can be identified by the analysis of resonance condition. The proposed method is applied to test systems, and the results illustrate its capabilities.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.335-338
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• 2003

In this paper, we considered the design of gain scheduled controllers for linear systems with input constraints. The gain scheduled control is a method that uses larger control gain when the states are smaller, and smaller gain when it is larger. By doing this, we can use a full actuator capacity. Also we allow the over-saturation in control to improve the performance. First, we derive a control and a reachable set expressed as LMI form, while minimizing the $L_2$ gain from the disturbance to the measured output. Next, the reachable set is divided as nested subsets, and the control gains are obtained by minimizing the $L_2$ gain at each nested subset. Finally, the control gains are scheduled according to the status of states, i.e., the nested-subset in which the states are located. Performance of the proposed technique is illustrated through simulations of a six-story building subject to earthquake ground motion.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.11
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• pp.1620-1627
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• 2017

In this paper, we design an observer-based $H_{\infty}$ fuzzy controller for interval type-2 Takagi-Sugeno (T-S) fuzzy systems under imperfect premise matching. The designed observer-based controller can effectively estimate the state of the system and make fuzzy system satisfy the $H_{\infty}$ disturbance attenuation performance. Using the slack matrix, the derived stabilization condition is expressed in terms of a linear matrix inequality. Finally, the effectiveness of the proposed method is verified through a simulation example.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.187-187
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• 1999

in order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment system is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulation are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.40-45
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• 2016

This paper presents the neuro-fuzzy control method for balancing a two-wheel mobile robot. A two-wheel mobile robot is built for the experimental studies. On-line learning algorithm based on the back-propagation(BP) method is derived for the Takagi-Sugeno(T-S) neuro-fuzzy controller. The modified error is proposed to learn the B-P algorithm for the balancing control of a two-wheel mobile robot. The T-S controller is implemented on a DSP chip. Experimental studies of the balancing control performance are conducted. Balancing control performances with disturbance are also conducted and results are evaluated.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.8
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• pp.735-741
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• 2015

In this paper, we suggest a dimensional controller to produce a different thickness strip without adding production facilities at the same steel. We describe the model for the non-linear thickness and speed setup, and drive a variation of the speed and thickness with Talyor expansion. The control algorithm is composed of 8 steps and the transient condition is added in order to maintain a mass flow between stands. A simulator is developed in order to verify the algorithm, and includes a non-linear rolling model, the tension model, AGC model, the disturbance model, and so on. From the simulation results by disturbances, we show that the thickness, tension and looper angle are converged to the set condition when we change the rolling conditions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.185-190
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• 2009

A scheme of expert system based on iterative learning control is proposed. Iterative learning control can obtain control experience from the historical data to build the knowledge base of expert system. Considering some uncertain factors, a flexible measure is adopted in iterative learning control (ILC). Simulation proves the feasibility and effect of the air conditioning control expert system based on flexible iterative learning control (F-ILC). Finally, a feedback compensation unit is incorporated against irregular heavy disturbance.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.1
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• pp.90-97
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• 2001

In this paper, a novel speed control strategy using an acceleration feedforward compensation by the estimation of the system inertia is proposed. With the proposed method, the enhanced speed control performance can be achieved and the speed response against the disturbance torque can be improved for the vector-controled induction motor drive systems in which the bandwidth of the speed controller cannot be made large enough. The simulation and experimental results for induction motor drive systems confirm the validity of the proposed strategy.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.4
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• pp.191-200
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• 2000

In practical applications, we frequently encounter the actuator nonlinearity in control systems, and its representative nonlinearity is saturation. A controller designed without considering this saturation nonlinearity is often a source of degradation of performance. To treat the saturation nonlinearity more efficiently, we adopt the multiplicative decomposition and the additive decomposition. Based on these decompositions, we present two controller design methods in the LMI(Linear Matrix Inequality) form that guarantee the L2 gain, from the disturbance to the measured output, is less than or equal to a given value. Finally, we give two examples to show the applicability and usefulness of our results.