• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.704-707
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• 1997

In this paper, we present a fuzzy logic based tuner for continuous on-line tuning of PID controllers. The essential idea of the scheme is to parameterize a Ziegler-Nichols-like tuning formula by a singler parameter .alpha., then to use an on line fuzzy logic to self-tune the parameter. The adaptive scaling makes the controller robust against large variations in parametric and dynamics uncertainties in the plant model. New self-tuning controller has the ability to decide when to use PI or PID control by extracting process dynamics from relay experiments. These scheme lead to improved performance of the transient and steady state behavior of the closed loop system, including processes with nonminimum phase processes.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.1062-1067
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• 2005

This paper discusses a skeleton feature extraction method for ubiquitous robot system. The skeleton features are used to analyze human motion and pose estimation. In different conventional feature extraction environment, the ubiquitous robot system requires more robust feature extraction method because it has internal vibration and low image quality. The new hybrid silhouette extraction method and adaptive skeleton model are proposed to overcome this constrained environment. The skin color is used to extract more sophisticated feature points. Finally, the experimental results show the superiority of the proposed method.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.213-216
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• 2002

This paper describes a new approach to estimate induction motor speed from terminal voltages and currents for speed-sensorless vector control. This algorithm is based on Model Reference Adaptive System(MRAS). The proposed technique is simple and robust to the variation of motor parameters. Specially, this algorithm is not affected by the variation of stator resistance and it does not require any pure integration at all. The validity of this new approach is proved by simulations.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.357-359
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• 1994

This paper consists of the vector control of three phase induction motors which has robustness against disturbances and parameter variations by the TDOF (Two Degree Of Freedom) theory. Using the TDOF theory, induction motor is robustly controlled for resistance variations and disturbances. Adaptive observer is used for the purpose of estimating the stator fluxes, the stator currents, and the parameters. The proposed control netted is verified by computer simulations.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.1053-1055
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• 2000

This paper describes a new approach to estimating permanent magnet synchronous motor(PMSM) speed and position from measured terminal voltages and currents for speed-sensorless vector control. The proposed system is based on observing the instantaneous reactive power of the motor. The described technique is very simple and robust to variations of motor parameters. The new approach is not dependent upon the value of the stator resistance. Also, MRAS schemes are chosen for determining the adaptive law for the speed and the position estimator. The effectiveness is verified by simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.304-309
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• 1998

In this paper, a fuzzy controller for stabilization of the inverted pendulum system is propose. The facility of this fuzzy controller which has a swing-up control mode and a stabilization one, moves a pendulum in an initial natural stable equilibrium point and a cart in arbitary position, to an unstable equilibrium point and a center of rail. Specially, the virtual equilibrium point ($\Phi$veq) which describes functionally considers the interactive dynamics between a position of cart and a angle of inverted pendulum is introduced. And comparing with the convention optimal controller, the proposed hierarchical fuzzy inference structur made substantially the inverted pendulum system robust and stable.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.293-300
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• 2011

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator and AFLC is confirmed by comparing to conventional algorithm.

• Proceedings of the KIEE Conference
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• 2000.11b
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• pp.384-386
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• 2000

This paper describes a new approach to estimating permanent magnet synchronous motor(PMSM) speed and position from measured terminal voltages and currents for speed-sensorless vector control. The proposed system is based on observing the instantaneous reactive power of the motor. The described technique is very simple and robust to variations of motor parameters. The new approach is not dependent upon the value of the stator resistance. Also, MRAS schemes are chosen for determining the adaptive law for the speed and the position estimator. The effectiveness is verified by the experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3177-3195
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• 2014

In many moving object detection problems of an aerial video, accurate and robust stabilization is of critical importance. In this paper, a novel accurate image alignment algorithm for aerial electronic image stabilization (EIS) is described. The feature points are first selected using optimal derivative filters based Harris detector, which can improve differentiation accuracy and obtain the precise coordinates of feature points. Then we choose the Delaunay Triangulation edges to find the matching pairs between feature points in overlapping images. The most "useful" matching points that belong to the background are used to find the global transformation parameters using the projective invariant. Finally, intentional motion of the camera is accumulated for correction by Sage-Husa adaptive filtering. Experiment results illustrate that the proposed algorithm is applied to the aerial captured video sequences with various dynamic scenes for performance demonstrations.

• Earthquakes and Structures
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• v.22 no.4
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• pp.421-430
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• 2022

This paper addresses the stochastic control problem of robots within the framework of parameter uncertainty and uncertain noise covariance. First of all, an open circle deterministic trajectory optimization issue is explained without knowing the unequivocal type of the dynamical framework. Then, a Linear Quadratic Gaussian (LQG) controller is intended for the ostensible trajectory-dependent linearized framework, to such an extent that robust hereditary NN robotic controller made out of the Kalman filter and the fuzzy controller is blended to ensure the asymptotic stability of the non-continuous controlled frameworks. Applicability and performance of the proposed algorithm shown through simulation results in the complex systems which are demonstrate the feasible to improve the performance by the proposed approach.